Web Scraping and Sentiment Analysis.ipynb

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  "metadata": {
    "colab": {
      "name": "Scraping the reviews of books.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMdp3eMj0yTHqEcPr/ICw61"
    },
    "kernelspec": {
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          "height": 496
        },
        "outputId": "ca8b233b-878b-43d9-c6d9-96734d2725dd"
      },
      "source": [
        "#installation of the required libraries\n",
        "\n",
        "!pip install googletrans\n",
        "!pip install openpyxl\n",
        "!pip install TextBlob\n",
        "!pip install gspread\n",
        "!pip install --upgrade plotly"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: contextvars>=2.1; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from sniffio->httpx==0.13.3->googletrans) (2.4)\n",
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            "Requirement already satisfied: immutables>=0.9 in /usr/local/lib/python3.6/dist-packages (from contextvars>=2.1; python_version < \"3.7\"->sniffio->httpx==0.13.3->googletrans) (0.14)\n",
            "Requirement already satisfied: hyperframe<6,>=5.2.0 in /usr/local/lib/python3.6/dist-packages (from h2==3.*->httpcore==0.9.*->httpx==0.13.3->googletrans) (5.2.0)\n",
            "Requirement already satisfied: hpack<4,>=3.0 in /usr/local/lib/python3.6/dist-packages (from h2==3.*->httpcore==0.9.*->httpx==0.13.3->googletrans) (3.0.0)\n",
            "Requirement already satisfied: openpyxl in /usr/local/lib/python3.6/dist-packages (2.5.9)\n",
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            "Requirement already satisfied: TextBlob in /usr/local/lib/python3.6/dist-packages (0.15.3)\n",
            "Requirement already satisfied: nltk>=3.1 in /usr/local/lib/python3.6/dist-packages (from TextBlob) (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from nltk>=3.1->TextBlob) (1.15.0)\n",
            "Requirement already satisfied: gspread in /usr/local/lib/python3.6/dist-packages (3.0.1)\n",
            "Requirement already satisfied: requests>=2.2.1 in /usr/local/lib/python3.6/dist-packages (from gspread) (2.23.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.2.1->gspread) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.2.1->gspread) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.2.1->gspread) (2020.6.20)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NGsKoJO88WgI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "3fd58891-c1d4-48b8-a9b5-89a88d36255a"
      },
      "source": [
        "#import all libraries\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import requests\n",
        "import urllib.request\n",
        "import time\n",
        "from bs4 import BeautifulSoup\n",
        "\n",
        "import re\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import pyplot\n",
        "%matplotlib inline\n",
        "import plotly.express as px\n",
        "\n",
        "import googletrans\n",
        "from googletrans import Translator\n",
        "\n",
        "import nltk\n",
        "nltk.download('vader_lexicon')\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "import textblob\n",
        "from textblob import TextBlob\n",
        "\n",
        "from google.colab import auth\n",
        "import gspread\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "from collections import OrderedDict, defaultdict, Counter\n",
        "import csv\n",
        "from sklearn.feature_extraction.text import CountVectorizer"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yTQFbEou6obO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#urls to be scraped\n",
        "URL = [('Year of the Elephant 1','https://www.goodreads.com/book/show/245256.Year_of_the_Elephant','reviewText stacked'),\n",
        "       ('Year of the Elephant 2','https://www.goodreads.com/book/show/6809360-year-of-the-elephant?rating=4&utm_medium=api&utm_source=book_widget','reviewText stacked'),\n",
        "       ('Celestial Bodies 1','https://www.goodreads.com/book/show/39653535-celestial-bodies','reviewText stacked'),\n",
        "       ('Celestial Bodies 1','https://www.goodreads.com/book/show/39653535-celestial-bodies','reviewText mediumText description readable'),\n",
        "       ('Celestial Bodies 2','https://www.goodreads.com/review/show/2909065240','reviewText mediumText description readable'),\n",
        "       ('Celestial Bodies 3','https://www.goodreads.com/review/show/2909065240','reviewText stacked'),\n",
        "       ('Celestial Bodies 4','https://www.goodreads.com/review/show/2748676786','reviewText mediumText description readable'),\n",
        "       ('Celestial Bodies 5','https://www.goodreads.com/review/show/2748676786','reviewText stacked'),\n",
        "       ('Celestial Bodies 6','https://www.goodreads.com/review/show/2748655998','reviewText mediumText description readable'),\n",
        "       ('Celestial Bodies 7','https://www.goodreads.com/review/show/2748655998','reviewText stacked'),\n",
        "       ('The Woman from Tantoura 1','https://www.goodreads.com/review/show/1160668061','reviewText stacked'),\n",
        "       ('The Woman from Tantoura 2','https://www.goodreads.com/review/show/1160668061','reviewText mediumText description readable'),\n",
        "       ('The Woman from Tantoura 3','https://www.goodreads.com/book/show/8239301','reviewText stacked'),\n",
        "       ('The Woman from Tantoura 4','https://www.goodreads.com/book/show/17678418-the-woman-from-tantoura','reviewText stacked'),\n",
        "       ('The Woman from Tantoura 5','https://www.goodreads.com/review/show/112611057','reviewText mediumText description readable'),\n",
        "       ('Granada 1','https://www.goodreads.com/book/show/218562.Granada','reviewText stacked'),\n",
        "       ('Granada 2','https://www.goodreads.com/book/show/218562.Granada','reviewText mediumText description readable'),\n",
        "       ('Granada 3','https://www.goodreads.com/en/book/show/3438000','reviewText stacked'),\n",
        "       ('Granada 4','https://www.goodreads.com/fr/book/show/27269009-the-woman-from-tantoura','reviewText mediumText description readable'),\n",
        "       ('Granada 5','https://www.goodreads.com/book/show/17678418-the-woman-from-tantoura','reviewText stacked'),\n",
        "       ('Granada 6','https://www.goodreads.com/review/show/1221720588','reviewText stacked'),\n",
        "       ('Granada 7','https://www.goodreads.com/review/show/1221720588','reviewText mediumText description readable'),\n",
        "       ('Granada 8','https://www.goodreads.com/review/show/81642717','reviewText stacked'),\n",
        "       ('Granada 9','https://www.goodreads.com/review/show/81642717','reviewText mediumText description readable'),\n",
        "       ('Blue Lorries','https://www.goodreads.com/book/show/18049202-blue-lorries','reviewText mediumText description readable'),\n",
        "       ('The House on Arnus Square','https://www.goodreads.com/book/show/3544759-the-house-on-arnus-square','reviewText stacked'),\n",
        "       ('A compass for the sunflower','https://www.goodreads.com/book/show/2328001.A_Compass_for_the_Sunflower','reviewText mediumText description readable'),\n",
        "       ('A Balcony Over The Fakihani 1','https://www.goodreads.com/book/show/4534538-a-balcony-over-the-fakihani','reviewText stacked'),\n",
        "       ('A Balcony Over The Fakihani 2','https://www.goodreads.com/book/show/4534538-a-balcony-over-the-fakihani','reviewText mediumText description readable'),\n",
        "       ('A Balcony Over The Fakihani 3','https://www.goodreads.com/book/show/286782.A_Balcony_Over_the_Fakihani','reviewText stacked'),\n",
        "       ('A Balcony Over The Fakihani 4','https://www.goodreads.com/book/show/286782.A_Balcony_Over_the_Fakihani','reviewText mediumText description readable'),\n",
        "       ('A Balcony Over The Fakihani 5','https://www.goodreads.com/review/show/3174682387','reviewText mediumText description readable'),\n",
        "       ('The Eye of the Mirror 1','https://www.goodreads.com/book/show/5792114-the-eye-of-the-mirror','reviewText stacked'),\n",
        "       ('The Eye of the Mirror 2','https://www.goodreads.com/book/show/5792114-the-eye-of-the-mirror','reviewText mediumText description readable'),\n",
        "       ('The Man from Bashmour','https://www.goodreads.com/book/show/1177207.The_Man_from_Bashmour','reviewText mediumText description readable'),       \n",
        "       ('The Wiles of Men and Other Stories 1','https://www.goodreads.com/book/show/1177205.The_Wiles_of_Men_and_Other_Stories','reviewText stacked'),      \n",
        "       ('The Wiles of Men and Other Stories 2','https://www.goodreads.com/review/show/398872587','reviewText mediumText description readable'),\n",
        "       ('The Wiles of Men and Other Stories 3','https://www.goodreads.com/book/show/4947328-the-wiles-of-men-and-other-stories','reviewText mediumText description readable'),\n",
        "       ('The Wiles of Men and Other Stories 4','https://www.goodreads.com/book/show/4947328-the-wiles-of-men-and-other-stories','rreviewText stacked'),\n",
        "       ('The Wiles of Men and Other Stories 5','https://www.goodreads.com/review/show/2247595169','reviewText mediumText description readable'),\n",
        "       ('The Stone of Laughter','http://www.librarything.com/work/1110230/reviews/160664538','commentText'),\n",
        "       ('The Tiller of the Waters 1','https://www.goodreads.com/book/show/4510433-the-tiller-of-the-waters','reviewText stacked'),   \n",
        "       ('The Tiller of the Waters 2','https://www.goodreads.com/book/show/4510433-the-tiller-of-the-waters','reviewText mediumText description readable'), \n",
        "       ('The Tiller of the Waters 3','https://arablit.org/2010/07/03/one-minute-review-hoda-barakats-tiller-of-waters/','entry-content'), \n",
        "       ('The Tiller of the Waters 4','https://mdbrady.wordpress.com/2017/04/17/the-tiller-of-waters-by-hoda-barakat/','entry clear'), \n",
        "       ('The Tiller of the Waters 5','https://thegoodbookcorner.com/2015/09/27/the-tiller-of-waters-by-hoda-barakat/','single'), \n",
        "       ('The Tiller of the Waters 6','https://readkutub.wordpress.com/2009/11/02/tiller-of-the-waters-harith-al-miyah-by-hoda-barakat/','entry-content'), \n",
        "       ('Disciples of passion 1','https://www.goodreads.com/book/show/1139212.Disciples_of_Passion','reviewText stacked'),\n",
        "       ('Disciples of passion 2','https://www.goodreads.com/book/show/1139212.Disciples_of_Passion','reviewText mediumText description readable'),\n",
        "       ('Algerian White: A Narrative 1','https://www.goodreads.com/review/show/2042990459','reviewText mediumText description readable'),\n",
        "       ('Algerian White: A Narrative 2','http://www.librarything.com/work/2021065/reviews/100655933','commentText'),\n",
        "       ('Children of the New World','http://www.librarything.com/work/1536931/reviews/132259156','commentText'),       \n",
        "       ('Fantasia: An Algerian Cavalcade 1','http://www.librarything.com/work/296224/reviews/156071930','commentText'),\n",
        "       ('Fantasia: An Algerian Cavalcade 2','https://www.goodreads.com/book/show/457864.Fantasia','reviewText stacked'),\n",
        "       ('Fantasia: An Algerian Cavalcade 3','https://www.goodreads.com/review/show/4598459','reviewText mediumText description readable'),\n",
        "       ('Fantasia: An Algerian Cavalcade 4','https://www.goodreads.com/review/show/2298343504','reviewText mediumText description readable'),\n",
        "       ('Fantasia: An Algerian Cavalcade 5','https://www.goodreads.com/review/show/2298343504','reviewText stacked'),\n",
        "       ('Fantasia: An Algerian Cavalcade 6','https://www.goodreads.com/review/show/1006690824','reviewText mediumText description readable'),\n",
        "       ('Fantasia: An Algerian Cavalcade 7','https://www.goodreads.com/book/show/457864.Fantasia','reviewText mediumText description readable'),\n",
        "       ('Fantasia: An Algerian Cavalcade 8','https://www.goodreads.com/book/show/74700.Fantasia','reviewText mediumText description readable'),\n",
        "       ('Fantasia: An Algerian Cavalcade 9','https://www.goodreads.com/book/show/74700.Fantasia','reviewText stacked'),\n",
        "       ('A Sister to Scheherazade 1','https://www.goodreads.com/book/show/388218.A_Sister_to_Scheherazade_Emerging_Voices','reviewText stacked'),\n",
        "       ('A Sister to Scheherazade 2','https://www.goodreads.com/book/show/388218.A_Sister_to_Scheherazade_Emerging_Voices','reviewText mediumText description readable'),\n",
        "       ('A Sister to Scheherazade 3','https://www.goodreads.com/review/show/1071490491','reviewText mediumText description readable'),\n",
        "       ('A Sister to Scheherazade 4','https://www.goodreads.com/review/show/681424353','reviewText mediumText description readable'),\n",
        "       ('A Sister to Scheherazade 5','https://www.goodreads.com/book/show/937191.A_Sister_to_Scheherazade','reviewText stacked'),\n",
        "       ('A Sister to Scheherazade 6','https://www.goodreads.com/book/show/937191.A_Sister_to_Scheherazade','reviewText mediumText description readable'),\n",
        "       ('A Sister to Scheherazade 7','https://caughtbetweenthepages.wordpress.com/tag/a-sister-to-scheherazade/','entry-content'),\n",
        "       ('A Sister to Scheherazade 8','http://howlingfrog.blogspot.com/2018/08/a-sister-to-scheherazade-wit-month-and.html','post-body entry-content'),\n",
        "       ('Women of Algiers in Their Apartment 1','https://www.goodreads.com/book/show/4688851-women-of-algiers-in-their-apartment','reviewText stacked'),\n",
        "       ('Women of Algiers in Their Apartment 2','https://www.goodreads.com/book/show/4688851-women-of-algiers-in-their-apartment','reviewText mediumText description readable'),\n",
        "       ('Women of Algiers in Their Apartment 3','http://www.librarything.com/work/378749/reviews/82106703','commentText'),\n",
        "       ('Women of Algiers in Their Apartment 4','https://www.goodreads.com/review/show/913987416','reviewText mediumText description readable'),\n",
        "       ('Velvet 1','https://www.goodreads.com/book/show/48571883-velvet','reviewText mediumText description readable'),\n",
        "       ('Velvet 2','https://www.goodreads.com/book/show/48571883-velvet','reviewText stacked'),\n",
        "       ('Sabriya: Damascus Bitter Sweet 1','https://www.goodreads.com/book/show/1092712.Sabriya','reviewText stacked'),\n",
        "       ('Sabriya: Damascus Bitter Sweet 2','https://www.goodreads.com/book/show/1092712.Sabriya','reviewText mediumText description readable'),\n",
        "       ('Sabriya: Damascus Bitter Sweet 4','https://www.goodreads.com/en/book/show/10224031','reviewText stacked'),\n",
        "       ('''Grandfather's Tale 1''','https://www.goodreads.com/review/show/2649106838','reviewText mediumText description readable'),\n",
        "       ('''Grandfather's Tale 2''','https://www.goodreads.com/book/show/290136.Grandfather_s_Tale','reviewText stacked'),\n",
        "       ('''Grandfather's Tale 3''','https://www.goodreads.com/book/show/290136.Grandfather_s_Tale','reviewText mediumText description readable'),\n",
        "       ('American Granddaughter','https://www.goodreads.com/book/show/15793844-the-american-granddaughter','reviewText mediumText description readable'),\n",
        "       ('The End of Spring 1','https://www.goodreads.com/book/show/1175926.The_End_of_Spring','reviewText stacked'),\n",
        "       ('The End of Spring 2','https://www.goodreads.com/book/show/1175926.The_End_of_Spring','reviewText mediumText description readable'),\n",
        "       ('The Inheritance 1','https://www.goodreads.com/book/show/218443.The_Inheritance','reviewText stacked'),\n",
        "       ('The Inheritance 2','https://www.goodreads.com/book/show/218443.The_Inheritance','reviewText mediumText description readable'),\n",
        "       ('The Inheritance 3','https://www.goodreads.com/book/show/218443.The_Inheritance?rating=2&utm_medium=api&utm_source=blog_book','reviewText mediumText description readable'),\n",
        "       ('The Inheritance 4','https://arabicbookworm.wordpress.com/2016/12/06/the-inheritance/','entry-content'),\n",
        "       ('Of Noble Origins: A Modern Palestinian Novel 1','https://www.goodreads.com/book/show/12697984-of-noble-origins','reviewText stacked'),\n",
        "       ('Of Noble Origins: A Modern Palestinian Novel 2','https://www.goodreads.com/book/show/12697984-of-noble-origins','reviewText mediumText description readable'),\n",
        "       ('The Image, the Icon, and the Covenant','https://www.goodreads.com/book/show/1175927.The_Image_the_Icon_and_the_Covenant','reviewText stacked'),\n",
        "       ('The Image, the Icon, and the Covenant','https://www.goodreads.com/book/show/1175927.The_Image_the_Icon_and_the_Covenant','reviewText mediumText description readable'),\n",
        "       ('Wild Thorns (Saqi) 1','http://www.librarything.com/work/28895/reviews/24886133','commentText'),\n",
        "       ('Wild Thorns (Saqi) 2','https://www.goodreads.com/review/show/1676144','reviewText mediumText description readable'),\n",
        "       ('Wild Thorns (Saqi) 3','https://www.goodreads.com/review/show/1623104161','reviewText mediumText description readable'),\n",
        "       ('Wild Thorns (Saqi) 4','https://www.goodreads.com/book/show/780880.Wild_Thorns','reviewText mediumText description readable'),\n",
        "       ('Wild Thorns (Saqi) 5','https://www.goodreads.com/book/show/780880.Wild_Thorns','reviewText stacked'),\n",
        "       ('Wild Thorns (Saqi) 6','https://www.goodreads.com/book/show/3839957-wild-thorns','reviewText mediumText description readable'),\n",
        "       ('Wild Thorns (Saqi) 7','https://www.goodreads.com/book/show/3839957-wild-thorns','reviewText stacked'),\n",
        "       ('A Sky So Close 1','http://www.librarything.com/work/373648/reviews/32163656','commentText'),\n",
        "       ('A Sky So Close 2','https://www.goodreads.com/book/show/1452240.A_Sky_So_Close','reviewText stacked'), \n",
        "       ('A Sky So Close 2','https://www.goodreads.com/book/show/1452240.A_Sky_So_Close','reviewText mediumText description readable'),  \n",
        "       ('Absent 1','https://www.goodreads.com/book/show/2600673','reviewText stacked'),\n",
        "       ('Absent 2','https://www.goodreads.com/review/show/146503031','reviewText mediumText description readable'),\n",
        "       ('Absent 3','https://www.fantasticfiction.com/k/betool-khedairi/absent.htm#:~:text=Tightly%20crafted%20and%20skillfully%20told,resilience%20of%20the%20human%20spirit.','blurb'),\n",
        "       ('Absent 4','https://www.goodreads.com/book/show/1446454.Absent','reviewText stacked'),\n",
        "       ('Absent 5','https://www.goodreads.com/book/show/1446454.Absent','reviewText mediumText description readable'),\n",
        "       ('Absent 6','http://www.farmlanebooks.co.uk/2011/absent-betool-khedairi/','entry-content'),\n",
        "       ('Absent 7','http://www.farmlanebooks.co.uk/2011/absent-betool-khedairi/','comments-inner section-inner thin max-percentage'),\n",
        "       ('Mothballs 1','https://www.goodreads.com/book/show/935032.Mothballs','reviewText stacked'),   \n",
        "       ('Mothballs 2','https://www.goodreads.com/book/show/935032.Mothballs','reviewText mediumText description readable'),  \n",
        "       ('Naphtalene: A Novel of Baghdad 1','https://www.goodreads.com/book/show/642182.Naphtalene','reviewText stacked'),   \n",
        "       ('Naphtalene: A Novel of Baghdad 2','https://www.goodreads.com/book/show/642182.Naphtalene','reviewText mediumText description readable'),  \n",
        "       ('The Loved Ones 1','https://www.goodreads.com/book/show/1968589.The_Loved_Ones','reviewText stacked'),\n",
        "       ('The Loved Ones 2','https://www.goodreads.com/book/show/1968589.The_Loved_Ones','reviewText mediumText description readable'),\n",
        "       ('The Loved Ones 3','https://www.goodreads.com/en/book/show/27473549-the-loved-ones','reviewText stacked'),\n",
        "       ('The Loved Ones 4','https://www.goodreads.com/en/book/show/27473549-the-loved-ones','reviewText mediumText description readable'),\n",
        "       ('Memory in the Flesh 1','https://www.goodreads.com/book/show/426499.Memory_in_the_Flesh','reviewText stacked'),\n",
        "       ('Memory in the Flesh 2','https://www.goodreads.com/book/show/426499.Memory_in_the_Flesh','reviewText mediumText description readable'),\n",
        "       ('Memory in the Flesh 3','https://www.goodreads.com/review/show/1302627506','reviewText mediumText description readable'),\n",
        "       ('Memory in the Flesh 4','https://www.goodreads.com/review/show/174425915','reviewText mediumText description readable'),\n",
        "       ('Memory in the Flesh 5','https://www.goodreads.com/book/show/2738211-memory-in-the-flesh','reviewText stacked'),\n",
        "       ('Memory in the Flesh 6','https://www.goodreads.com/book/show/2738211-memory-in-the-flesh','reviewText mediumText description readable'),\n",
        "       ('Memory in the Flesh 7','https://bookertalk.com/memory-in-the-flesh-book-review/','entry clear-block'),\n",
        "       ('Memory in the Flesh 8','https://www.goodreads.com/review/show/2413473823','reviewText mediumText description readable'),\n",
        "       ('The Art of Forgetting 1','https://www.goodreads.com/book/show/21254858-the-art-of-forgetting','reviewText stacked'),\n",
        "       ('The Art of Forgetting 2','https://www.goodreads.com/book/show/21254858-the-art-of-forgetting','reviewText mediumText description readable'),\n",
        "       ('The Art of Forgetting 3','https://web.archive.org/web/20140423213335/http://www.banipal.co.uk/book_reviews/88/the-art-of-forgetting-by-ahlem-mosteghanemi/','page_content selections_page_content'),\n",
        "       ('Chaos of the Senses 1','https://www.goodreads.com/en/book/show/2501455._','reviewText stacked'),   \n",
        "       ('Chaos of the Senses 2','https://www.goodreads.com/review/show/1520499883','reviewText mediumText description readable'), \n",
        "       ('Chaos of the Senses 3','https://www.goodreads.com/book/show/23921864-chaos-of-the-senses','reviewText stacked'),   \n",
        "       ('Chaos of the Senses 4','https://www.goodreads.com/book/show/23921864-chaos-of-the-senses','reviewText mediumText description readable'), \n",
        "       ('Chaos of the Senses 5','https://www.goodreads.com/book/show/2389865.Chaos_of_the_Senses','reviewText stacked'), \n",
        "       ('Chaos of the Senses 6','https://www.goodreads.com/book/show/2389865.Chaos_of_the_Senses','reviewText mediumText description readable'), \n",
        "       ('Flight Against Time 1','https://www.goodreads.com/book/show/2145978.Flight_Against_Time','reviewText stacked'),\n",
        "       ('Flight Against Time 2','https://www.goodreads.com/book/show/2350641.Flight_Against_Time?from_search=true&rating=1&search_version=service','reviewText mediumText description readable'),\n",
        "       ('''Zubaida's Window 1''','https://www.goodreads.com/review/show/30841242','reviewText mediumText description readable'),\n",
        "       ('''Zubaida's Window 2''','https://www.goodreads.com/book/show/3065015-zubaida-s-window','reviewText stacked'),\n",
        "       ('''Zubaida's Window 3''','https://www.goodreads.com/book/show/3065015-zubaida-s-window','reviewText mediumText description readable'),\n",
        "       ('Leaves of Narcissus','https://www.goodreads.com/book/show/532386.Leaves_of_Narcissus?rating=4','reviewText mediumText description readable'),     \n",
        "       ('Distant View of a Minaret and Other Stories 1','https://www.goodreads.com/review/show/1264646788','reviewText stacked'),\n",
        "       ('Distant View of a Minaret and Other Stories 2','https://www.goodreads.com/review/show/1264646788','reviewText mediumText description readable'),\n",
        "       ('Distant View of a Minaret and Other Stories 3','https://www.goodreads.com/review/show/811136695','reviewText stacked'),\n",
        "       ('Distant View of a Minaret and Other Stories 4','https://www.goodreads.com/review/show/811136695','reviewText mediumText description readable'),\n",
        "       ('Distant View of a Minaret and Other Stories 5','https://www.goodreads.com/review/show/885032747','reviewText stacked'),\n",
        "       ('Distant View of a Minaret and Other Stories 6','https://www.goodreads.com/review/show/885032747','reviewText mediumText description readable'),\n",
        "       ('Distant View of a Minaret and Other Stories 7','https://www.goodreads.com/book/show/23530034-distant-view-of-a-minaret-and-other-stories','reviewText stacked'),\n",
        "       ('Distant View of a Minaret and Other Stories 8','https://www.goodreads.com/book/show/23530034-distant-view-of-a-minaret-and-other-stories','reviewText mediumText description readable'), \n",
        "       ('Distant View of a Minaret and Other Stories 2','http://www.librarything.com/work/155334/reviews/150026368','commentText'),\n",
        "       ('Memoirs of a Woman Doctor 1','http://www.librarything.com/work/408928/reviews/150026351','commentText'),\n",
        "       ('Memoirs of a Woman Doctor 2','https://www.goodreads.com/review/show/2770186308','reviewText stacked'),\n",
        "       ('Memoirs of a Woman Doctor 3','https://www.goodreads.com/book/show/27282252-memoirs-of-a-woman-doctor','reviewText mediumText description readable'),\n",
        "       ('Memoirs of a Woman Doctor 4','https://www.goodreads.com/book/show/27282252-memoirs-of-a-woman-doctor','reviewText stacked'),\n",
        "       ('Memoirs of a Woman Doctor 5','https://www.goodreads.com/review/show/1844769553','reviewText mediumText description readable'),\n",
        "       ('Memoirs of a Woman Doctor 6','https://www.goodreads.com/book/show/14290073','reviewText stacked'),\n",
        "       ('Memoirs of a Woman Doctor 7','https://www.goodreads.com/book/show/14290073','reviewText mediumText description readable'),\n",
        "       ('Memoirs of a Woman Doctor 6','https://www.goodreads.com/book/show/817660.Memoirs_of_a_Woman_Doctor','reviewText stacked'),\n",
        "       ('Memoirs of a Woman Doctor 7','https://www.goodreads.com/book/show/817660.Memoirs_of_a_Woman_Doctor','reviewText mediumText description readable'),\n",
        "       ('Memoirs of a Woman Doctor 8','https://blogs.bmj.com/medical-humanities/2015/11/11/the-reading-room-a-review-of-memoirs-of-a-woman-doctor/','entry-content'),\n",
        "       ('Memoirs of a Woman Doctor 9','http://thebluebookcase.blogspot.com/2011/09/review-memoirs-of-woman-doctor-by-nawal.html','post-body entry-content'),\n",
        "       ('Memoirs of a Woman Doctor 10','http://biblibio.blogspot.com/2016/08/witmonth-day-13-memoirs-of-woman-doctor.html','post-body entry-content'),\n",
        "       ('Woman at Point Zero 1','http://www.librarything.com/work/124574/reviews/158221380','commentText'),\n",
        "       ('Woman at Point Zero 2','https://www.goodreads.com/review/show/655634592','reviewText stacked'),\n",
        "       ('Woman at Point Zero 3','https://www.goodreads.com/review/show/655634592','reviewText mediumText description readable'),\n",
        "       ('Woman at Point Zero 4','https://www.goodreads.com/review/show/863236797','reviewText stacked'),\n",
        "       ('Woman at Point Zero 5','https://www.goodreads.com/review/show/863236797','reviewText mediumText description readable'),\n",
        "       ('Woman at Point Zero 6','https://www.goodreads.com/review/show/3392164848','reviewText stacked'),\n",
        "       ('Woman at Point Zero 7','https://www.goodreads.com/review/show/3392164848','reviewText mediumText description readable'),\n",
        "       ('Woman at Point Zero 8','https://www.goodreads.com/book/show/159604.Woman_at_Point_Zero','reviewText stacked'),\n",
        "       ('Woman at Point Zero 9','https://www.goodreads.com/review/show/655634592','reviewText mediumText description readable'),\n",
        "       ('Woman at Point Zero 10','https://www.goodreads.com/book/show/1022540.Woman_at_Point_Zero','reviewText stacked'),\n",
        "       ('Woman at Point Zero 11','https://www.goodreads.com/book/show/1022540.Woman_at_Point_Zero','reviewText mediumText description readable'),\n",
        "       ('Love in the Kingdom of Oil 1','https://www.goodreads.com/book/show/7848478','reviewText stacked'),\n",
        "       ('Love in the Kingdom of Oil 2','https://www.goodreads.com/book/show/7848478','reviewText mediumText description readable'),\n",
        "       ('Love in the Kingdom of Oil 3','https://www.goodreads.com/book/show/426345.Love_in_the_Kingdom_of_Oil','reviewText stacked'),\n",
        "       ('Love in the Kingdom of Oil 4','https://www.goodreads.com/book/show/426345.Love_in_the_Kingdom_of_Oil','reviewText mediumText description readable'),\n",
        "       ('Love in the Kingdom of Oil 5','https://www.goodreads.com/review/show/1336469865','reviewText mediumText description readable'),\n",
        "       ('Love in the Kingdom of Oil 6','https://kinnareads.com/2011/11/03/love-in-the-kingdom-of-oil-nawal-el-saadawi/','entry-content'),      \n",
        "       ('Beirut 75- A Novel','https://books.google.com/books?id=j3pyniooGX8C&sitesec=reviews','reviewText mediumText description readable'),\n",
        "       ('Girls of Riyadh 1','https://www.bookreporter.com/reviews/girls-of-riyadh','article'),\n",
        "       ('Girls of Riyadh 2','https://www.goodreads.com/review/show/10893811','reviewText stacked'),\n",
        "       ('Girls of Riyadh 3','https://www.goodreads.com/review/show/10893811','reviewText mediumText description readable'),\n",
        "       ('Girls of Riyadh 4','https://www.goodreads.com/review/show/19743296','reviewText stacked'),\n",
        "       ('Girls of Riyadh 5','https://www.goodreads.com/review/show/19743296','reviewText mediumText description readable'),\n",
        "       ('Girls of Riyadh 6','https://www.goodreads.com/review/show/25889970','reviewText stacked'),\n",
        "       ('Girls of Riyadh 7','https://www.goodreads.com/review/show/25889970','reviewText mediumText description readable'),\n",
        "       ('Girls of Riyadh 8','https://www.goodreads.com/book/show/7889012-girls-of-riyadh','reviewText stacked'),\n",
        "       ('Girls of Riyadh 9','https://www.goodreads.com/book/show/7889012-girls-of-riyadh','reviewText mediumText description readable'),\n",
        "       ('Girls of Riyadh 10','https://www.goodreads.com/book/show/398812.Girls_of_Riyadh','reviewText stacked'),\n",
        "       ('Girls of Riyadh 11','https://www.goodreads.com/book/show/398812.Girls_of_Riyadh','reviewText mediumText description readable'),\n",
        "       ('Beirut Blues 1','http://www.librarything.com/work/419343/reviews/83693221','commentText'),\n",
        "       ('Beirut Blues 2','https://www.goodreads.com/review/show/287863051','reviewText stacked'),\n",
        "       ('Beirut Blues 3','https://www.goodreads.com/review/show/287863051','reviewText mediumText description readable'),\n",
        "       ('Beirut Blues 4','https://www.goodreads.com/book/show/937271.Beirut_Blues','reviewText stacked'),\n",
        "       ('Beirut Blues 5','https://www.goodreads.com/book/show/937271.Beirut_Blues','reviewText mediumText description readable'),\n",
        "       ('Beirut Blues 6','https://www.goodreads.com/review/show/1106528770','reviewText mediumText description readable'),\n",
        "       ('Only in London 1','http://www.librarything.com/work/38609/reviews/110138145','commentText'),\n",
        "       ('Only in London 2','https://www.goodreads.com/book/show/95282.Only_in_London','reviewText stacked'),\n",
        "       ('Only in London 3','https://www.goodreads.com/book/show/95282.Only_in_London','reviewText mediumText description readable'),\n",
        "       ('Only in London 4','https://www.goodreads.com/book/show/937297.Only_In_London','reviewText stacked'),\n",
        "       ('Only in London 5','https://www.goodreads.com/book/show/937297.Only_In_London','reviewText mediumText description readable'),\n",
        "       ('Only in London 6','https://www.goodreads.com/review/show/484254943','reviewText mediumText description readable'),\n",
        "       ('Only in London 7','https://www.goodreads.com/review/show/87356762','reviewText stacked'),\n",
        "       ('Only in London 8','https://www.goodreads.com/review/show/87356762','reviewText mediumText description readable'),\n",
        "       ('Only in London 9','https://www.goodreads.com/review/show/1020553334','reviewText mediumText description readable'),\n",
        "       ('The Occasional Virgin 1','http://www.librarything.com/work/20936118/reviews/158218857','commentText'),\n",
        "       ('The Occasional Virgin 2','https://www.goodreads.com/review/show/2421914066','reviewText stacked'),\n",
        "       ('The Occasional Virgin 3','https://www.goodreads.com/review/show/2421914066','reviewText mediumText description readable'),\n",
        "       ('The Occasional Virgin 4','https://www.goodreads.com/review/show/2428044590','reviewText stacked'),\n",
        "       ('The Occasional Virgin 5','https://www.goodreads.com/review/show/2428044590','reviewText mediumText description readable'),\n",
        "       ('The Occasional Virgin 6','https://www.goodreads.com/review/show/2460328217','reviewText mediumText description readable'),\n",
        "       ('The Occasional Virgin 5','https://www.goodreads.com/book/show/36750071-the-occasional-virgin','reviewText stacked'),\n",
        "       ('The Occasional Virgin 6','https://www.goodreads.com/book/show/36750071-the-occasional-virgin','reviewText mediumText description readable'),\n",
        "       ('The Story of Zahra 1','http://www.librarything.com/work/86224/reviews/95377021','commentText'),       \n",
        "       ('The Story of Zahra 2','https://www.goodreads.com/review/show/3393761684','reviewText stacked'), \n",
        "       ('The Story of Zahra 3','https://www.goodreads.com/review/show/3393761684','reviewText mediumText description readable'),   \n",
        "       ('The Story of Zahra 4','https://www.goodreads.com/book/show/937270.The_Story_of_Zahra','reviewText stacked'), \n",
        "       ('The Story of Zahra 5','https://www.goodreads.com/book/show/937270.The_Story_of_Zahra','reviewText mediumText description readable'),         \n",
        "       ('Women of Sand and Myrrh 1','http://www.librarything.com/work/51124/reviews/170424372','commentText'),\n",
        "       ('Women of Sand and Myrrh 2','https://www.goodreads.com/review/show/1008862520','reviewText stacked'),\n",
        "       ('Women of Sand and Myrrh 3','https://www.goodreads.com/review/show/1008862520','reviewText mediumText description readable'),\n",
        "       ('Women of Sand and Myrrh 4','https://www.goodreads.com/review/show/401065494','reviewText mediumText description readable'),\n",
        "       ('Women of Sand and Myrrh 5','https://www.goodreads.com/book/show/358627.Women_of_Sand_and_Myrrh','reviewText stacked'),\n",
        "       ('Women of Sand and Myrrh 6','https://www.goodreads.com/book/show/178173.Women_of_Sand_and_Myrrh','reviewText stacked'),\n",
        "       ('The Blue Aubergine 1','https://www.goodreads.com/book/show/387666.Blue_Aubergine','reviewText stacked'),\n",
        "       ('The Blue Aubergine 2','https://www.goodreads.com/book/show/387666.Blue_Aubergine','reviewText mediumText description readable'),       \n",
        "       ('Brooklyn Heights 1','http://www.librarything.com/work/12299043/reviews/99909518','commentText'),  \n",
        "       ('Brooklyn Heights 2','https://www.goodreads.com/book/show/11728757-brooklyn-heights','reviewText stacked'),  \n",
        "       ('Brooklyn Heights 3','https://www.goodreads.com/book/show/11728757-brooklyn-heights','reviewText mediumText description readable'), \n",
        "       ('Brooklyn Heights 4','https://www.goodreads.com/review/show/3005872537','reviewText mediumText description readable'), \n",
        "       ('Brooklyn Heights 5','https://www.goodreads.com/review/show/1780010639','reviewText mediumText description readable'),\n",
        "       ('Brooklyn Heights 6','https://www.goodreads.com/review/show/139658103','reviewText mediumText description readable'), \n",
        "       ('Dunyazad','https://www.goodreads.com/book/show/804190.Dunyazad','reviewText mediumText description readable'),  \n",
        "       ('Daughter of Damascus 1','https://www.goodreads.com/book/show/310108.Daughter_of_Damascus','reviewText stacked'),\n",
        "       ('Daughter of Damascus 2','https://www.goodreads.com/book/show/310108.Daughter_of_Damascus','reviewText mediumText description readable'),\n",
        "       ('Daughter of Damascus 3','https://www.goodreads.com/review/show/811895423','reviewText mediumText description readable'),\n",
        "       ('Daughter of Damascus 4','https://www.goodreads.com/review/show/10415044','reviewText mediumText description readable'),\n",
        "       ('B as in Beirut 1','https://www.goodreads.com/book/show/2192812.B_as_in_Beirut','reviewText stacked'),\n",
        "       ('B as in Beirut 2','https://www.goodreads.com/book/show/2192812.B_as_in_Beirut','reviewText mediumText description readable'),\n",
        "       ('B as in Beirut 3','http://www.librarything.com/work/7765761/reviews/141111690','commentText'),   \n",
        "       ('Red Wine 1','https://www.goodreads.com/book/show/8606517-red-wine','reviewText stacked'), \n",
        "       ('Red Wine 2','https://www.goodreads.com/book/show/8606517-red-wine','reviewText mediumText description readable'), \n",
        "       ('Red Wine 3','https://www.goodreads.com/review/show/1541053595','reviewText mediumText description readable'),\n",
        "       ('The Open Door 1','http://www.librarything.com/work/1599267/reviews/69097043','commentText'),\n",
        "       ('The Open Door 2','https://www.goodreads.com/review/show/2294359797','reviewText mediumText description readable'),\n",
        "       ('The Open Door 3','https://www.goodreads.com/review/show/2080893976','reviewText mediumText description readable'),\n",
        "       ('The Open Door 4','https://www.goodreads.com/review/show/275428317','reviewText mediumText description readable'),\n",
        "       ('The Open Door 5','https://www.goodreads.com/book/show/31175878-the-open-door','reviewText stacked'),\n",
        "       ('The Open Door 6','https://www.goodreads.com/book/show/31175878-the-open-door','reviewText mediumText description readable'),\n",
        "       ('So Vast the Prison','https://www.goodreads.com/book/show/446284.So_Vast_the_Prison','reviewText stacked')]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0k3Spk4hJR5A",
        "colab_type": "text"
      },
      "source": [
        "Scraping Data using BeautifulSoup "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C0_20-CRowh9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "book_dict = {}\n",
        "for value in URL:\n",
        "  book_review = []\n",
        "  response = requests.get(value[1])\n",
        "  soup = BeautifulSoup(response.text, \"html.parser\")\n",
        "  for comments in soup.findAll('div', attrs={'class':value[2]}):\n",
        "    comment = comments.text\n",
        "    book_review.append(comment)\n",
        "  review = list(set(book_review))\n",
        "  book_dict[value[0]] = book_review"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YuAkJoalr4Y_",
        "colab_type": "text"
      },
      "source": [
        "#### **Sentimental Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HROtXPRpqpkI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataframe_reviews = pd.DataFrame(book_dict.items(), columns=['Book', 'Review'])\n",
        "dataframe_reviews['Reviews'] = [','.join(map(str, l)) for l in dataframe_reviews['Review']]\n",
        "dataframe_reviews = dataframe_reviews[['Book','Reviews']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2k7LxFtd49G-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "review_df = pd.concat([pd.Series(row['Book'], row['Reviews'].split('\\n\\n,'))for _, row in dataframe_reviews.iterrows()]).reset_index()\n",
        "review_df.rename(columns={0:'Book','index':'Review'}, inplace=True)\n",
        "review_df['Review'] = review_df['Review'].str.replace('\\n', '')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7rwSxLio5EbU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f1608230-13e1-4d4f-c1c2-c59329dde5bb"
      },
      "source": [
        "review_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Review</th>\n",
              "      <th>Book</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The independence of Abouzeid's subtitle is bot...</td>\n",
              "      <td>Year of the Elephant 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>عند انتقاء رواية لأشتريها من المكتبة، لفت اسم ...</td>\n",
              "      <td>Year of the Elephant 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>It shows the conflicting visions of colonial i...</td>\n",
              "      <td>Year of the Elephant 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>\" زهرة\" تشق طريقها كالنهر يعود إلى مجراه نقيا ...</td>\n",
              "      <td>Year of the Elephant 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>عام الفيل مجموعة قصصية تتكون من ٩ قصص متنوعة و...</td>\n",
              "      <td>Year of the Elephant 1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              Review                    Book\n",
              "0  The independence of Abouzeid's subtitle is bot...  Year of the Elephant 1\n",
              "1  عند انتقاء رواية لأشتريها من المكتبة، لفت اسم ...  Year of the Elephant 1\n",
              "2  It shows the conflicting visions of colonial i...  Year of the Elephant 1\n",
              "3  \" زهرة\" تشق طريقها كالنهر يعود إلى مجراه نقيا ...  Year of the Elephant 1\n",
              "4  عام الفيل مجموعة قصصية تتكون من ٩ قصص متنوعة و...  Year of the Elephant 1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HdGg50TleZW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#detecting english phrases\n",
        "\n",
        "def helper(a):\n",
        "  translator = Translator()\n",
        "  lang = translator.detect(a)\n",
        "  return lang.lang"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOZrLLGZluDC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "review_df['Lang_Review'] = review_df['Review'].apply(helper)\n",
        "df_review = review_df[review_df['Lang_Review'] == 'en']\n",
        "df_review_emtn = df_review.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K0jL6MpGJGkb",
        "colab_type": "text"
      },
      "source": [
        "#### **Vader Sentiment Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqxPwGejsSzz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sid = SentimentIntensityAnalyzer()\n",
        "def get_sentiment(text:str, analyser,desired_type:str='pos'):\n",
        "    # Get sentiment from text\n",
        "    sentiment_score = analyser.polarity_scores(text)\n",
        "    return sentiment_score[desired_type]\n",
        "\n",
        "# Get Sentiment scores\n",
        "def get_sentiment_scores(df,data_column):\n",
        "    df[f'{data_column} Positive Sentiment Score'] = df[data_column].astype(str).apply(lambda x: get_sentiment(x,sid,'pos'))\n",
        "    df[f'{data_column} Negative Sentiment Score'] = df[data_column].astype(str).apply(lambda x: get_sentiment(x,sid,'neg'))\n",
        "    df[f'{data_column} Neutral Sentiment Score'] = df[data_column].astype(str).apply(lambda x: get_sentiment(x,sid,'neu'))\n",
        "    df[f'{data_column} Compound Sentiment Score'] = df[data_column].astype(str).apply(lambda x: get_sentiment(x,sid,'compound'))\n",
        "    return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QmqH7KOvEPjL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "text_sentiment = get_sentiment_scores(df_review,'Review')\n",
        "del text_sentiment['Lang_Review']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0s4G07z7Mt5b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def remove_numbers(x):\n",
        "  output = re.sub(r'\\d+', '', x)\n",
        "  return output"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TsqIvBvIMxaY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "text_sentiment['Book']=text_sentiment['Book'].apply(remove_numbers)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4b9UW0QVSfZ4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "fd590be3-f468-485d-8f37-660325e4a43c"
      },
      "source": [
        "text_sentiment.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Review</th>\n",
              "      <th>Book</th>\n",
              "      <th>Review Positive Sentiment Score</th>\n",
              "      <th>Review Negative Sentiment Score</th>\n",
              "      <th>Review Neutral Sentiment Score</th>\n",
              "      <th>Review Compound Sentiment Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The independence of Abouzeid's subtitle is bot...</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>0.050</td>\n",
              "      <td>0.059</td>\n",
              "      <td>0.891</td>\n",
              "      <td>0.6827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>It shows the conflicting visions of colonial i...</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>0.063</td>\n",
              "      <td>0.147</td>\n",
              "      <td>0.790</td>\n",
              "      <td>-0.7491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Leila Abouzeid's novella, ‘Ām al-fīl / Year of...</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>0.140</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.791</td>\n",
              "      <td>0.9711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Maybe this would have had more of an impact on...</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>0.143</td>\n",
              "      <td>0.031</td>\n",
              "      <td>0.826</td>\n",
              "      <td>0.9805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>I used this course in an introductory anthropo...</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>0.226</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.774</td>\n",
              "      <td>0.9666</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              Review  ... Review Compound Sentiment Score\n",
              "0  The independence of Abouzeid's subtitle is bot...  ...                          0.6827\n",
              "2  It shows the conflicting visions of colonial i...  ...                         -0.7491\n",
              "5  Leila Abouzeid's novella, ‘Ām al-fīl / Year of...  ...                          0.9711\n",
              "6  Maybe this would have had more of an impact on...  ...                          0.9805\n",
              "7  I used this course in an introductory anthropo...  ...                          0.9666\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iojNzp0IGo2d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Polarity where -1 is the most negative and 1 is the most positive\n",
        "# Subjectivity 0.0 is very objective and 1.0 is very subjective\n",
        "\n",
        "df_review_emtn[['polarity', 'subjectivity']] = df_review_emtn['Review'].apply(lambda Text: pd.Series(TextBlob(Text).sentiment))\n",
        "del df_review_emtn['Lang_Review']\n",
        "df_review_emtn['Book']=df_review_emtn['Book'].apply(remove_numbers)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWM0u5TZStcm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "3fe8961b-25bd-44f3-eda9-31a6b9922b87"
      },
      "source": [
        "df_review_emtn.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Review</th>\n",
              "      <th>Book</th>\n",
              "      <th>polarity</th>\n",
              "      <th>subjectivity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The independence of Abouzeid's subtitle is bot...</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>0.096389</td>\n",
              "      <td>0.537111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>It shows the conflicting visions of colonial i...</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>0.024074</td>\n",
              "      <td>0.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Leila Abouzeid's novella, ‘Ām al-fīl / Year of...</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>-0.053535</td>\n",
              "      <td>0.456782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Maybe this would have had more of an impact on...</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>0.117667</td>\n",
              "      <td>0.445333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>I used this course in an introductory anthropo...</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>0.346667</td>\n",
              "      <td>0.583810</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              Review  ... subjectivity\n",
              "0  The independence of Abouzeid's subtitle is bot...  ...     0.537111\n",
              "2  It shows the conflicting visions of colonial i...  ...     0.400000\n",
              "5  Leila Abouzeid's novella, ‘Ām al-fīl / Year of...  ...     0.456782\n",
              "6  Maybe this would have had more of an impact on...  ...     0.445333\n",
              "7  I used this course in an introductory anthropo...  ...     0.583810\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZECC1FuHNTZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_new_agg = df_review_emtn.groupby(['Book']).mean().reset_index()\n",
        "df_txt_sent = text_sentiment.groupby(['Book']).mean().reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6f57rzwSIJRZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_new_agg.to_excel(r'aggregated_emotions.xlsx', index = False, header=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuGE_EjtKV-G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5b359b1e-c880-4365-9826-3b14e5e830b1"
      },
      "source": [
        "df_new_agg.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Book</th>\n",
              "      <th>polarity</th>\n",
              "      <th>subjectivity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A Balcony Over The Fakihani</td>\n",
              "      <td>0.244569</td>\n",
              "      <td>0.488629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A Sister to Scheherazade</td>\n",
              "      <td>0.055439</td>\n",
              "      <td>0.419181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A Sky So Close</td>\n",
              "      <td>-0.009091</td>\n",
              "      <td>0.240530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A compass for the sunflower</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Absent</td>\n",
              "      <td>0.088672</td>\n",
              "      <td>0.482417</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           Book  polarity  subjectivity\n",
              "0  A Balcony Over The Fakihani   0.244569      0.488629\n",
              "1     A Sister to Scheherazade   0.055439      0.419181\n",
              "2               A Sky So Close  -0.009091      0.240530\n",
              "3   A compass for the sunflower  0.000000      0.000000\n",
              "4                       Absent   0.088672      0.482417"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrNLp_JETd4t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "ff73f6fc-f73e-452c-9a49-c0966237825c"
      },
      "source": [
        "df_txt_sent.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Book</th>\n",
              "      <th>Review Positive Sentiment Score</th>\n",
              "      <th>Review Negative Sentiment Score</th>\n",
              "      <th>Review Neutral Sentiment Score</th>\n",
              "      <th>Review Compound Sentiment Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A Balcony Over The Fakihani</td>\n",
              "      <td>0.139000</td>\n",
              "      <td>0.096385</td>\n",
              "      <td>0.610692</td>\n",
              "      <td>0.182515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A Sister to Scheherazade</td>\n",
              "      <td>0.103441</td>\n",
              "      <td>0.091059</td>\n",
              "      <td>0.746559</td>\n",
              "      <td>0.057221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A Sky So Close</td>\n",
              "      <td>0.041500</td>\n",
              "      <td>0.075500</td>\n",
              "      <td>0.383000</td>\n",
              "      <td>-0.491750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A compass for the sunflower</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Absent</td>\n",
              "      <td>0.149030</td>\n",
              "      <td>0.094212</td>\n",
              "      <td>0.741591</td>\n",
              "      <td>0.152542</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           Book  ...  Review Compound Sentiment Score\n",
              "0  A Balcony Over The Fakihani   ...                         0.182515\n",
              "1     A Sister to Scheherazade   ...                         0.057221\n",
              "2               A Sky So Close   ...                        -0.491750\n",
              "3   A compass for the sunflower  ...                         0.000000\n",
              "4                       Absent   ...                         0.152542\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmoUTD5NYfR3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#select the books\n",
        "array = ['The Blue Aubergine ','Beirut - A Novel','Improvisations on a Missing String','Red Wine ','Points of the Compass: Stories','Leaves of Narcissus',\\\n",
        "'The Inheritance ','The Open Door ','The Man from Bashmour','Daughter of Damascus ','Year of the Elephant ','Fantasia: An Algerian Cavalcade ','Children of the New World']\n",
        "\n",
        "df_txt_sent_plot = df_txt_sent.loc[df_txt_sent['Book'].isin(array)]\n",
        "df_new_agg_plot = df_new_agg.loc[df_new_agg['Book'].isin(array)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZPn5tOp2cF6h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "ae50f97b-1ef7-4d45-c434-ef0ddd810bf5"
      },
      "source": [
        "df_txt_sent_plot"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Book</th>\n",
              "      <th>Review Positive Sentiment Score</th>\n",
              "      <th>Review Negative Sentiment Score</th>\n",
              "      <th>Review Neutral Sentiment Score</th>\n",
              "      <th>Review Compound Sentiment Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Beirut - A Novel</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Children of the New World</td>\n",
              "      <td>0.107000</td>\n",
              "      <td>0.108000</td>\n",
              "      <td>0.784000</td>\n",
              "      <td>-0.982200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Daughter of Damascus</td>\n",
              "      <td>0.072000</td>\n",
              "      <td>0.041333</td>\n",
              "      <td>0.720000</td>\n",
              "      <td>0.216150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Fantasia: An Algerian Cavalcade</td>\n",
              "      <td>0.104746</td>\n",
              "      <td>0.076075</td>\n",
              "      <td>0.774537</td>\n",
              "      <td>0.220861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Leaves of Narcissus</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Red Wine</td>\n",
              "      <td>0.174800</td>\n",
              "      <td>0.058000</td>\n",
              "      <td>0.567200</td>\n",
              "      <td>-0.101500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>The Blue Aubergine</td>\n",
              "      <td>0.032867</td>\n",
              "      <td>0.040400</td>\n",
              "      <td>0.860200</td>\n",
              "      <td>0.058007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>The Inheritance</td>\n",
              "      <td>0.096900</td>\n",
              "      <td>0.082400</td>\n",
              "      <td>0.620900</td>\n",
              "      <td>-0.135520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>The Man from Bashmour</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>The Open Door</td>\n",
              "      <td>0.186171</td>\n",
              "      <td>0.046029</td>\n",
              "      <td>0.739229</td>\n",
              "      <td>0.574449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>0.158407</td>\n",
              "      <td>0.058852</td>\n",
              "      <td>0.782815</td>\n",
              "      <td>0.522893</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                Book  ...  Review Compound Sentiment Score\n",
              "8                   Beirut - A Novel  ...                         0.000000\n",
              "14         Children of the New World  ...                        -0.982200\n",
              "15             Daughter of Damascus   ...                         0.216150\n",
              "19  Fantasia: An Algerian Cavalcade   ...                         0.220861\n",
              "24               Leaves of Narcissus  ...                         0.000000\n",
              "32                         Red Wine   ...                        -0.101500\n",
              "36               The Blue Aubergine   ...                         0.058007\n",
              "41                  The Inheritance   ...                        -0.135520\n",
              "43             The Man from Bashmour  ...                         0.000000\n",
              "45                    The Open Door   ...                         0.574449\n",
              "56             Year of the Elephant   ...                         0.522893\n",
              "\n",
              "[11 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xqWpBXvqY0yA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "outputId": "bed84c70-5565-48d0-ea41-c6514ec7c914"
      },
      "source": [
        "#Plotting polarity\n",
        "\n",
        "x_axis = np.arange(len(df_new_agg_plot['Book']))\n",
        "tick_locations = [value+0.4 for value in x_axis]\n",
        "fig,ax=plt.subplots(figsize=(10, 7))\n",
        "plt.xticks(tick_locations, df_new_agg_plot['Book'], rotation=\"vertical\")\n",
        "\n",
        "plot=plt.bar(df_new_agg_plot['Book'], df_new_agg_plot[\"polarity\"], color=(df_new_agg_plot['polarity'] > 0).map({True: 'g', False: 'r'}), alpha=1, align=\"edge\")\n",
        "plt.grid()\n",
        "\n",
        "#plt.title(\"Sentiment Analysis of Book Reviews \" )\n",
        "plt.title(\"Overall Sentiment Polarity based on Book Reviews(%s) \")\n",
        "plt.xlabel(\"Book\")\n",
        "plt.ylabel(\"Book Review Polarity\")\n",
        "ax.grid(linestyle=\"dotted\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZk5zSlzf2uj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "outputId": "8a129f85-7b07-48ec-8915-81400849b945"
      },
      "source": [
        "#Plotting subjectivity\n",
        "\n",
        "x_axis = np.arange(len(df_new_agg_plot['Book']))\n",
        "tick_locations = [value+0.4 for value in x_axis]\n",
        "fig,ax=plt.subplots(figsize=(10, 7))\n",
        "plt.xticks(tick_locations, df_new_agg_plot['Book'], rotation=\"vertical\")\n",
        "\n",
        "plot=plt.bar(df_new_agg_plot['Book'], df_new_agg_plot[\"subjectivity\"], color=(df_new_agg_plot['subjectivity'] > 0).map({True: 'g', False: 'r'}), alpha=1, align=\"edge\")\n",
        "plt.grid()\n",
        "\n",
        "#plt.title(\"Sentiment Analysis of Book Reviews (%s) \" )\n",
        "plt.title(\"Overall Sentiment Subjectivity based on Book Reviews(\")\n",
        "plt.xlabel(\"Book\")\n",
        "plt.ylabel(\"Book Review Subjectivity\")\n",
        "ax.grid(linestyle=\"dotted\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oXaHb54awvRY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "outputId": "6a4c72f1-80e9-4c0b-a460-747ee22b1707"
      },
      "source": [
        "#Plotting compound sentiment\n",
        "\n",
        "x_axis = np.arange(len(df_txt_sent_plot['Book']))\n",
        "tick_locations = [value+0.4 for value in x_axis]\n",
        "fig,ax=plt.subplots(figsize=(10, 7))\n",
        "plt.xticks(tick_locations, df_txt_sent_plot['Book'], rotation=\"vertical\")\n",
        "\n",
        "plot=plt.bar(df_txt_sent_plot['Book'], df_txt_sent_plot[\"Review Compound Sentiment Score\"], color=(df_txt_sent_plot['Review Compound Sentiment Score'] > 0).map({True: 'g', False: 'r'}), alpha=1, align=\"edge\")\n",
        "plt.grid()\n",
        "\n",
        "#plt.title(\"Compound Sentiments of Book Reviews\" )\n",
        "plt.title(\"Compound Sentiments of Book Reviews\")\n",
        "plt.xlabel(\"Book\")\n",
        "plt.ylabel(\"Compound Book Review\")\n",
        "ax.grid(linestyle=\"dotted\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQtCX4HlpDqf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        },
        "outputId": "2deb1052-aefd-4716-9773-4a72d5c638be"
      },
      "source": [
        "# Setting the positions and width for the bars\n",
        "pos = list(range(len(df_txt_sent_plot['Book'])))\n",
        "width = 0.35\n",
        "\n",
        "# Plotting the bars\n",
        "fig, ax = plt.subplots(figsize=(15,10))\n",
        "\n",
        "# Create a bar with positive sentiment data\n",
        "plt.bar(pos, df_txt_sent_plot['Review Positive Sentiment Score'], width, alpha=1, color='#ebab34')\n",
        "\n",
        "# Create a bar for negative\n",
        "plt.bar([p + width for p in pos], df_txt_sent_plot['Review Negative Sentiment Score'], width, alpha=1, color='#140be6')\n",
        "\n",
        "# Set the y and x axis label\n",
        "ax.set_ylabel('Score')\n",
        "ax.set_xlabel(\"Book\")\n",
        "\n",
        "# Set the labels for the x ticks\n",
        "ax.set_xticklabels(df_txt_sent_plot['Book'])\n",
        "\n",
        "# Set the position of the x ticks\n",
        "tick_locations = [p + 1.5 * width for p in pos]\n",
        "plt.xticks(tick_locations, df_txt_sent_plot['Book'], rotation=\"vertical\")\n",
        "\n",
        "# Setting the x-axis and y-axis limits\n",
        "plt.xlim(min(pos)-width, max(pos)+width*4)\n",
        "plt.ylim([0, max(df_txt_sent_plot['Review Positive Sentiment Score'] + df_txt_sent_plot['Review Negative Sentiment Score'])] )\n",
        "\n",
        "\n",
        "# Adding the legend and showing the plot\n",
        "plt.legend(['Positive', 'Negative'], loc='upper left')\n",
        "\n",
        "ax.grid(linestyle=\"dotted\")\n",
        "plt.title(\"Overall Positive and Negative Sentiments of Book Reviews\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "33_2F_K5w2u1",
        "colab_type": "text"
      },
      "source": [
        "#### **Publisher Reviews**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1FBZDOXKA6b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "auth.authenticate_user()\n",
        "\n",
        "gc = gspread.authorize(GoogleCredentials.get_application_default())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S3dGI8IuKvlF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wb = gc.open_by_url('file')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-L8hNPmIRYjm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sheet = wb.worksheet('Publisher Blurbs (NEW VERSION)')\n",
        "data = sheet.get_all_values()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5D1oVGsj_XE4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_review_pub = pd.DataFrame(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X83xLBU4GGIv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_header = df_review_pub.iloc[0]\n",
        "df_review_pub = df_review_pub[1:]\n",
        "df_review_pub.columns = new_header "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLw81ghEYK3n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "publisher_review = df_review_pub[['AUTHOR','TITLE','Editorial Blurbs/Blurbs by Outside Source','Reviewer source/name']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNiVpAOHc2bW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "publisher_review = publisher_review[publisher_review['Editorial Blurbs/Blurbs by Outside Source'] != '0']\n",
        "publisher_review['Editorial Blurbs/Blurbs by Outside Source'].replace('', np.nan, inplace=True)\n",
        "publisher_review.dropna(subset=['Editorial Blurbs/Blurbs by Outside Source'], inplace=True)\n",
        "publisher_review['TITLE'].replace('', np.nan, inplace=True)\n",
        "publisher_review.dropna(subset=['TITLE'], inplace=True)\n",
        "publisher_review['Editorial Blurbs'] = publisher_review['Editorial Blurbs/Blurbs by Outside Source'].str.replace('\"', '')\n",
        "del publisher_review['Editorial Blurbs/Blurbs by Outside Source']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xp_ApcQ_eRCQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "79969675-5897-4c88-bb33-5d0aa7056d30"
      },
      "source": [
        "df_pub_review = publisher_review.copy()\n",
        "publisher_review.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AUTHOR</th>\n",
              "      <th>TITLE</th>\n",
              "      <th>Reviewer source/name</th>\n",
              "      <th>Editorial Blurbs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Layla Abu Zayd</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>World Literature Today</td>\n",
              "      <td>At a time when there is growing interest in th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Layla Abu Zayd</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>Ahmed Abd al-Salam al-Bakkah, Moroccan author ...</td>\n",
              "      <td>Leila Abouzeid has created a new style, a mosa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Layla Abu Zayd</td>\n",
              "      <td>Last Chapter (Modern Arabic Writing)</td>\n",
              "      <td></td>\n",
              "      <td>... a fascinating glimpse into a world mostly ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Jokha Alharthi</td>\n",
              "      <td>Celestial Bodies</td>\n",
              "      <td>-Marcia Lynx Qualey, The National</td>\n",
              "      <td>A richly imagined, engaging and poetic insight...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Jokha Alharthi</td>\n",
              "      <td>Celestial Bodies</td>\n",
              "      <td>-Women In Translation'</td>\n",
              "      <td>`Compelling...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "0          AUTHOR  ...                                   Editorial Blurbs\n",
              "1  Layla Abu Zayd  ...  At a time when there is growing interest in th...\n",
              "2  Layla Abu Zayd  ...  Leila Abouzeid has created a new style, a mosa...\n",
              "3  Layla Abu Zayd  ...  ... a fascinating glimpse into a world mostly ...\n",
              "4  Jokha Alharthi  ...  A richly imagined, engaging and poetic insight...\n",
              "5  Jokha Alharthi  ...                                     `Compelling...\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WDxVOPWehHn7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "publisher_review_sentiment = publisher_review[['TITLE','Editorial Blurbs']]\n",
        "publisher_review_sentiment[['polarity', 'subjectivity']] = publisher_review_sentiment['Editorial Blurbs'].apply(lambda Text: pd.Series(TextBlob(Text).sentiment))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "En8Cq3OuhHgX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a955ea97-93e4-4bcb-92ba-feabbf53feaa"
      },
      "source": [
        "publisher_review_sentiment.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>TITLE</th>\n",
              "      <th>Editorial Blurbs</th>\n",
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              "      <th>subjectivity</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Year of the Elephant</td>\n",
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              "      <td>0.369697</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>0.725000</td>\n",
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              "    <tr>\n",
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              "      <td>Celestial Bodies</td>\n",
              "      <td>A richly imagined, engaging and poetic insight...</td>\n",
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              "      <td>0.300000</td>\n",
              "      <td>0.600000</td>\n",
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              "</div>"
            ],
            "text/plain": [
              "0                                 TITLE  ... subjectivity\n",
              "1                  Year of the Elephant  ...     0.000000\n",
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              "3  Last Chapter (Modern Arabic Writing)  ...     0.725000\n",
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              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjvXqWcrhHY3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "outputId": "b27a6e77-ad46-4b92-f41b-fd17f6eb08f2"
      },
      "source": [
        "get_sentiment_scores(df_pub_review,'Editorial Blurbs')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AUTHOR</th>\n",
              "      <th>TITLE</th>\n",
              "      <th>Reviewer source/name</th>\n",
              "      <th>Editorial Blurbs</th>\n",
              "      <th>Editorial Blurbs Positive Sentiment Score</th>\n",
              "      <th>Editorial Blurbs Negative Sentiment Score</th>\n",
              "      <th>Editorial Blurbs Neutral Sentiment Score</th>\n",
              "      <th>Editorial Blurbs Compound Sentiment Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Layla Abu Zayd</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>World Literature Today</td>\n",
              "      <td>At a time when there is growing interest in th...</td>\n",
              "      <td>0.159</td>\n",
              "      <td>0.132</td>\n",
              "      <td>0.709</td>\n",
              "      <td>-0.0516</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Layla Abu Zayd</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>Ahmed Abd al-Salam al-Bakkah, Moroccan author ...</td>\n",
              "      <td>Leila Abouzeid has created a new style, a mosa...</td>\n",
              "      <td>0.091</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.909</td>\n",
              "      <td>0.2500</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Layla Abu Zayd</td>\n",
              "      <td>Last Chapter (Modern Arabic Writing)</td>\n",
              "      <td></td>\n",
              "      <td>... a fascinating glimpse into a world mostly ...</td>\n",
              "      <td>0.280</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.720</td>\n",
              "      <td>0.5423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Jokha Alharthi</td>\n",
              "      <td>Celestial Bodies</td>\n",
              "      <td>-Marcia Lynx Qualey, The National</td>\n",
              "      <td>A richly imagined, engaging and poetic insight...</td>\n",
              "      <td>0.311</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.689</td>\n",
              "      <td>0.9184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Jokha Alharthi</td>\n",
              "      <td>Celestial Bodies</td>\n",
              "      <td>-Women In Translation'</td>\n",
              "      <td>`Compelling...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Latifah al-Zayyat</td>\n",
              "      <td>The Open Door</td>\n",
              "      <td>Naguib Mahfouz</td>\n",
              "      <td>Latifa al-Zayyat greatly helped all of us Egyp...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>Latifah al-Zayyat</td>\n",
              "      <td>The Open Door</td>\n",
              "      <td>Kirkus Reviews (Book Review Magazine)</td>\n",
              "      <td>Absorbing ... Superbly translated ... Arguably...</td>\n",
              "      <td>0.289</td>\n",
              "      <td>0.089</td>\n",
              "      <td>0.622</td>\n",
              "      <td>0.6705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>Latifah al-Zayyat</td>\n",
              "      <td>The Open Door</td>\n",
              "      <td>Choice</td>\n",
              "      <td>Recommended.</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.2023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>Latifah al-Zayyat</td>\n",
              "      <td>The Open Door</td>\n",
              "      <td>Al Jadid</td>\n",
              "      <td>A pioneering work on many levels.</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>Latifah al-Zayyat</td>\n",
              "      <td>The Open Door</td>\n",
              "      <td>Abdel Moneim Tallima</td>\n",
              "      <td>Not only a great novel, but a literary landmar...</td>\n",
              "      <td>0.111</td>\n",
              "      <td>0.277</td>\n",
              "      <td>0.612</td>\n",
              "      <td>-0.2910</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>122 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "0               AUTHOR  ... Editorial Blurbs Compound Sentiment Score\n",
              "1       Layla Abu Zayd  ...                                   -0.0516\n",
              "2       Layla Abu Zayd  ...                                    0.2500\n",
              "3       Layla Abu Zayd  ...                                    0.5423\n",
              "4       Jokha Alharthi  ...                                    0.9184\n",
              "5       Jokha Alharthi  ...                                    0.0000\n",
              "..                 ...  ...                                       ...\n",
              "147  Latifah al-Zayyat  ...                                    0.0000\n",
              "148  Latifah al-Zayyat  ...                                    0.6705\n",
              "149  Latifah al-Zayyat  ...                                    0.2023\n",
              "150  Latifah al-Zayyat  ...                                    0.0000\n",
              "151  Latifah al-Zayyat  ...                                   -0.2910\n",
              "\n",
              "[122 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRxNQo97hHRa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "c348fd7e-1e9a-42af-a0ed-f179273024b9"
      },
      "source": [
        "df_pub_review.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>TITLE</th>\n",
              "      <th>Reviewer source/name</th>\n",
              "      <th>Editorial Blurbs</th>\n",
              "      <th>Editorial Blurbs Positive Sentiment Score</th>\n",
              "      <th>Editorial Blurbs Negative Sentiment Score</th>\n",
              "      <th>Editorial Blurbs Neutral Sentiment Score</th>\n",
              "      <th>Editorial Blurbs Compound Sentiment Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Layla Abu Zayd</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>World Literature Today</td>\n",
              "      <td>At a time when there is growing interest in th...</td>\n",
              "      <td>0.159</td>\n",
              "      <td>0.132</td>\n",
              "      <td>0.709</td>\n",
              "      <td>-0.0516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Layla Abu Zayd</td>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>Ahmed Abd al-Salam al-Bakkah, Moroccan author ...</td>\n",
              "      <td>Leila Abouzeid has created a new style, a mosa...</td>\n",
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              "      <td>0.000</td>\n",
              "      <td>0.909</td>\n",
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              "    <tr>\n",
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              "      <td>Layla Abu Zayd</td>\n",
              "      <td>Last Chapter (Modern Arabic Writing)</td>\n",
              "      <td></td>\n",
              "      <td>... a fascinating glimpse into a world mostly ...</td>\n",
              "      <td>0.280</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.720</td>\n",
              "      <td>0.5423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Jokha Alharthi</td>\n",
              "      <td>Celestial Bodies</td>\n",
              "      <td>-Marcia Lynx Qualey, The National</td>\n",
              "      <td>A richly imagined, engaging and poetic insight...</td>\n",
              "      <td>0.311</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.689</td>\n",
              "      <td>0.9184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Jokha Alharthi</td>\n",
              "      <td>Celestial Bodies</td>\n",
              "      <td>-Women In Translation'</td>\n",
              "      <td>`Compelling...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "0          AUTHOR  ... Editorial Blurbs Compound Sentiment Score\n",
              "1  Layla Abu Zayd  ...                                   -0.0516\n",
              "2  Layla Abu Zayd  ...                                    0.2500\n",
              "3  Layla Abu Zayd  ...                                    0.5423\n",
              "4  Jokha Alharthi  ...                                    0.9184\n",
              "5  Jokha Alharthi  ...                                    0.0000\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6qon92oFC3YS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "publisher_review_sentiment_agg = publisher_review_sentiment.groupby(['TITLE']).mean().reset_index()\n",
        "df_pub_review_agg = df_pub_review.groupby(['TITLE']).mean().reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uqr9H40thHN7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "publisher_review_sentiment_agg.to_excel (r'publisher_review_sentiment.xlsx', index = False, header=True)\n",
        "df_pub_review_agg.to_excel (r'df_pub_review.xlsx', index = False, header=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMlynk-LNUci",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "42c43b80-cd82-48bf-8292-4c2d29ef48e9"
      },
      "source": [
        "df_pub_review_agg.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TITLE</th>\n",
              "      <th>Editorial Blurbs Positive Sentiment Score</th>\n",
              "      <th>Editorial Blurbs Negative Sentiment Score</th>\n",
              "      <th>Editorial Blurbs Neutral Sentiment Score</th>\n",
              "      <th>Editorial Blurbs Compound Sentiment Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A Balcony Over the Fakihani: Three Novellas, 1993</td>\n",
              "      <td>0.108000</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>0.632000</td>\n",
              "      <td>-0.62490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Absent (translated by Muhayman Jamil)</td>\n",
              "      <td>0.244000</td>\n",
              "      <td>0.135000</td>\n",
              "      <td>0.621000</td>\n",
              "      <td>0.04228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algerian White: A Narrative</td>\n",
              "      <td>0.046333</td>\n",
              "      <td>0.163333</td>\n",
              "      <td>0.790333</td>\n",
              "      <td>-0.29680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>American Granddaughter</td>\n",
              "      <td>0.153000</td>\n",
              "      <td>0.087000</td>\n",
              "      <td>0.760000</td>\n",
              "      <td>0.13810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Blue Lorries – 2015</td>\n",
              "      <td>0.110000</td>\n",
              "      <td>0.111000</td>\n",
              "      <td>0.778500</td>\n",
              "      <td>-0.00380</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "0                                              TITLE  ...  Editorial Blurbs Compound Sentiment Score\n",
              "0  A Balcony Over the Fakihani: Three Novellas, 1993  ...                                   -0.62490\n",
              "1              Absent (translated by Muhayman Jamil)  ...                                    0.04228\n",
              "2                        Algerian White: A Narrative  ...                                   -0.29680\n",
              "3                             American Granddaughter  ...                                    0.13810\n",
              "4                                Blue Lorries – 2015  ...                                   -0.00380\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9UiXHm_U1MRY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#select the books\n",
        "array = ['The Blue Aubergine','Beirut - A Novel','Improvisations on a Missing String','Red Wine','Points of the Compass: Stories','Leaves of Narcissus',\\\n",
        "'The Inheritance','The Open Door','The Man from Bashmour','Daughter of Damascus','Year of the Elephant','Fantasia: An Algerian Cavalcade','Children of the New World']\n",
        "\n",
        "df_pub_sent_plot = publisher_review_sentiment_agg.loc[publisher_review_sentiment_agg['TITLE'].isin(array)]\n",
        "df_pub_agg_plot = df_pub_review_agg.loc[df_pub_review_agg['TITLE'].isin(array)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AaIQRovk05Ln",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "outputId": "b3eb7454-3e76-4ee3-c9bb-6a834f191825"
      },
      "source": [
        "#Plotting polarity\n",
        "\n",
        "x_axis = np.arange(len(df_pub_sent_plot['TITLE']))\n",
        "tick_locations = [value+0.4 for value in x_axis]\n",
        "fig,ax=plt.subplots(figsize=(10, 7))\n",
        "plt.xticks(tick_locations, df_pub_sent_plot['TITLE'], rotation=\"vertical\")\n",
        "\n",
        "plot=plt.bar(df_pub_sent_plot['TITLE'], df_pub_sent_plot[\"polarity\"], color=(df_pub_sent_plot['polarity'] > 0).map({True: 'g', False: 'r'}), alpha=1, align=\"edge\")\n",
        "plt.grid()\n",
        "\n",
        "#plt.title(\"Sentiment Analysis of Book Reviews \" )\n",
        "plt.title(\"Overall Sentiment Polarity based on Book Reviews\")\n",
        "plt.xlabel(\"Book\")\n",
        "plt.ylabel(\"Book Review Polarity\")\n",
        "ax.grid(linestyle=\"dotted\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7bIT8nH1xV-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "outputId": "25e5a50d-6abc-4bc5-ac06-697f4d0cff87"
      },
      "source": [
        "#Plotting polarity\n",
        "\n",
        "x_axis = np.arange(len(df_pub_sent_plot['TITLE']))\n",
        "tick_locations = [value+0.4 for value in x_axis]\n",
        "fig,ax=plt.subplots(figsize=(10, 7))\n",
        "plt.xticks(tick_locations, df_pub_sent_plot['TITLE'], rotation=\"vertical\")\n",
        "\n",
        "plot=plt.bar(df_pub_sent_plot['TITLE'], df_pub_sent_plot[\"subjectivity\"], color=(df_pub_sent_plot['subjectivity'] > 0).map({True: 'g', False: 'r'}), alpha=1, align=\"edge\")\n",
        "plt.grid()\n",
        "\n",
        "#plt.title(\"Sentiment Analysis of Book Reviews \" )\n",
        "plt.title(\"Overall Sentiment Subjectivity based on Book Reviews\")\n",
        "plt.xlabel(\"Book\")\n",
        "plt.ylabel(\"Book Review Subjectivity\")\n",
        "ax.grid(linestyle=\"dotted\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KCDeQyR32T3p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "outputId": "6736a132-da7c-4830-d792-3161c5ee93a7"
      },
      "source": [
        "#Plotting compound sentiment\n",
        "\n",
        "x_axis = np.arange(len(df_pub_agg_plot['TITLE']))\n",
        "tick_locations = [value+0.4 for value in x_axis]\n",
        "fig,ax=plt.subplots(figsize=(10, 7))\n",
        "plt.xticks(tick_locations, df_pub_agg_plot['TITLE'], rotation=\"vertical\")\n",
        "\n",
        "plot=plt.bar(df_pub_agg_plot['TITLE'], df_pub_agg_plot[\"Editorial Blurbs Compound Sentiment Score\"], color=(df_pub_agg_plot['Editorial Blurbs Compound Sentiment Score'] > 0).map({True: 'g', False: 'r'}), alpha=1, align=\"edge\")\n",
        "plt.grid()\n",
        "\n",
        "#plt.title(\"Compound Sentiments of Book Reviews\" )\n",
        "plt.title(\"Compound Sentiments of Book Reviews\")\n",
        "plt.xlabel(\"Book\")\n",
        "plt.ylabel(\"Compound Book Review\")\n",
        "ax.grid(linestyle=\"dotted\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mrIDgs7s3Bo1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "outputId": "5c7f0d79-c5b2-4e42-bd90-8204f7f20b8c"
      },
      "source": [
        "# Setting the positions and width for the bars\n",
        "pos = list(range(len(df_pub_agg_plot['TITLE'])))\n",
        "width = 0.35\n",
        "\n",
        "# Plotting the bars\n",
        "fig, ax = plt.subplots(figsize=(15,10))\n",
        "\n",
        "# Create a bar with positive sentiment data\n",
        "plt.bar(pos, df_pub_agg_plot['Editorial Blurbs Negative Sentiment Score'], width, alpha=1, color='#ebab34')\n",
        "\n",
        "# Create a bar for negative\n",
        "plt.bar([p + width for p in pos], df_pub_agg_plot['Editorial Blurbs Positive Sentiment Score'], width, alpha=1, color='#140be6')\n",
        "\n",
        "# Set the y and x axis label\n",
        "ax.set_ylabel('Score')\n",
        "ax.set_xlabel(\"Book\")\n",
        "\n",
        "# Set the labels for the x ticks\n",
        "ax.set_xticklabels(df_pub_agg_plot['TITLE'])\n",
        "\n",
        "# Set the position of the x ticks\n",
        "tick_locations = [p + 1.5 * width for p in pos]\n",
        "plt.xticks(tick_locations, df_pub_agg_plot['TITLE'], rotation=\"vertical\")\n",
        "\n",
        "# Setting the x-axis and y-axis limits\n",
        "plt.xlim(min(pos)-width, max(pos)+width*4)\n",
        "plt.ylim([0, max(df_pub_agg_plot['Editorial Blurbs Negative Sentiment Score'] + df_pub_agg_plot['Editorial Blurbs Positive Sentiment Score'])] )\n",
        "\n",
        "\n",
        "# Adding the legend and showing the plot\n",
        "plt.legend(['Negative', 'Positive'], loc='upper left')\n",
        "\n",
        "ax.grid(linestyle=\"dotted\")\n",
        "plt.title(\"Overall Positive and Negative Sentiments of Book Reviews\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xiMihv4_GAeI",
        "colab_type": "text"
      },
      "source": [
        "#### **NRC Emotion** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZHQH63UIP5N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "publisher_emotion = publisher_review[['TITLE','Editorial Blurbs']]\n",
        "reviewer_emotion = df_review[['Book','Review']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kf9E4ZYGMKsF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_nrc_data():\n",
        "    nrc = \"/content/NRC-emotion-lexicon-wordlevel-alphabetized-v0.92.txt\"\n",
        "    count=0\n",
        "    emotion_dict=dict()\n",
        "    with open(nrc,'r') as f:\n",
        "        all_lines = list()\n",
        "        for line in f:\n",
        "            if count < 46:\n",
        "                count+=1\n",
        "                continue\n",
        "            line = line.strip().split('\\t')\n",
        "            if int(line[2]) == 1:\n",
        "                if emotion_dict.get(line[0]):\n",
        "                    emotion_dict[line[0]].append(line[1])\n",
        "                else:\n",
        "                    emotion_dict[line[0]] = [line[1]]\n",
        "    return emotion_dict\n",
        "\n",
        "\n",
        "def emotion_analyzer(text,emotion_dict=get_nrc_data()):\n",
        "    #Set up the result dictionary\n",
        "    emotions = {x for y in emotion_dict.values() for x in y}\n",
        "    emotion_count = dict()\n",
        "    for emotion in emotions:\n",
        "        emotion_count[emotion] = 0\n",
        "\n",
        "    #Analyze the text and normalize by total number of words\n",
        "    total_words = len(text.split())\n",
        "    for word in text.split():\n",
        "        if emotion_dict.get(word):\n",
        "            for emotion in emotion_dict.get(word):\n",
        "                emotion_count[emotion] += 1/len(text.split())\n",
        "    return emotion_count"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqlBclsvWOlt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "publisher_emotion[['sadness','surprise','positive','joy','negative','anticipation','disgust','fear','anger','trust']] = \\\n",
        "publisher_emotion['Editorial Blurbs'].apply(lambda Text: pd.Series(emotion_analyzer(Text)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zw_KEG9JGGQm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reviewer_emotion[['sadness','surprise','positive','joy','negative','anticipation','disgust','fear','anger','trust']] = \\\n",
        "reviewer_emotion['Review'].apply(lambda Text: pd.Series(emotion_analyzer(Text)))\n",
        "\n",
        "reviewer_emotion['Book']=reviewer_emotion['Book'].apply(remove_numbers)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "huH8DNUTWkqD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "4d6d035b-cf1f-4799-e267-2b62b77ee2a5"
      },
      "source": [
        "publisher_emotion.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TITLE</th>\n",
              "      <th>Editorial Blurbs</th>\n",
              "      <th>sadness</th>\n",
              "      <th>surprise</th>\n",
              "      <th>positive</th>\n",
              "      <th>joy</th>\n",
              "      <th>negative</th>\n",
              "      <th>anticipation</th>\n",
              "      <th>disgust</th>\n",
              "      <th>fear</th>\n",
              "      <th>anger</th>\n",
              "      <th>trust</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>At a time when there is growing interest in th...</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.040000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>Leila Abouzeid has created a new style, a mosa...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Last Chapter (Modern Arabic Writing)</td>\n",
              "      <td>... a fascinating glimpse into a world mostly ...</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.083333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Celestial Bodies</td>\n",
              "      <td>A richly imagined, engaging and poetic insight...</td>\n",
              "      <td>0.026316</td>\n",
              "      <td>0.026316</td>\n",
              "      <td>0.026316</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.026316</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.026316</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.026316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Celestial Bodies</td>\n",
              "      <td>`Compelling...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "0                                 TITLE  ...     trust\n",
              "1                  Year of the Elephant  ...  0.040000\n",
              "2                  Year of the Elephant  ...  0.000000\n",
              "3  Last Chapter (Modern Arabic Writing)  ...  0.083333\n",
              "4                      Celestial Bodies  ...  0.026316\n",
              "5                      Celestial Bodies  ...  0.000000\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqvnTbR3Fmt9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "611b714c-11ac-4a6a-e31e-d003bbfd4b86"
      },
      "source": [
        "reviewer_emotion.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Book</th>\n",
              "      <th>Review</th>\n",
              "      <th>sadness</th>\n",
              "      <th>surprise</th>\n",
              "      <th>positive</th>\n",
              "      <th>joy</th>\n",
              "      <th>negative</th>\n",
              "      <th>anticipation</th>\n",
              "      <th>disgust</th>\n",
              "      <th>fear</th>\n",
              "      <th>anger</th>\n",
              "      <th>trust</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>The independence of Abouzeid's subtitle is bot...</td>\n",
              "      <td>0.030471</td>\n",
              "      <td>0.058172</td>\n",
              "      <td>0.016620</td>\n",
              "      <td>0.008310</td>\n",
              "      <td>0.024931</td>\n",
              "      <td>0.016620</td>\n",
              "      <td>0.030471</td>\n",
              "      <td>0.033241</td>\n",
              "      <td>0.019391</td>\n",
              "      <td>0.041551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>It shows the conflicting visions of colonial i...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.012821</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.025641</td>\n",
              "      <td>0.025641</td>\n",
              "      <td>0.038462</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.012821</td>\n",
              "      <td>0.038462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>Leila Abouzeid's novella, ‘Ām al-fīl / Year of...</td>\n",
              "      <td>0.020057</td>\n",
              "      <td>0.054441</td>\n",
              "      <td>0.008596</td>\n",
              "      <td>0.017192</td>\n",
              "      <td>0.020057</td>\n",
              "      <td>0.022923</td>\n",
              "      <td>0.037249</td>\n",
              "      <td>0.017192</td>\n",
              "      <td>0.020057</td>\n",
              "      <td>0.042980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>Maybe this would have had more of an impact on...</td>\n",
              "      <td>0.025974</td>\n",
              "      <td>0.043290</td>\n",
              "      <td>0.025974</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004329</td>\n",
              "      <td>0.004329</td>\n",
              "      <td>0.038961</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.047619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Year of the Elephant</td>\n",
              "      <td>I used this course in an introductory anthropo...</td>\n",
              "      <td>0.035294</td>\n",
              "      <td>0.023529</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.011765</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.023529</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.023529</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Book  ...     trust\n",
              "0  Year of the Elephant   ...  0.041551\n",
              "2  Year of the Elephant   ...  0.038462\n",
              "5  Year of the Elephant   ...  0.042980\n",
              "6  Year of the Elephant   ...  0.047619\n",
              "7  Year of the Elephant   ...  0.023529\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-l2AhF-a9j2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_agg_emtn = publisher_emotion.groupby(['TITLE']).mean().reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7xBAKd37cfZ8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "90623a67-a866-46c6-cbc1-4c677fc4de02"
      },
      "source": [
        "df_agg_emtn.head() #publisher reviews"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TITLE</th>\n",
              "      <th>sadness</th>\n",
              "      <th>surprise</th>\n",
              "      <th>positive</th>\n",
              "      <th>joy</th>\n",
              "      <th>negative</th>\n",
              "      <th>anticipation</th>\n",
              "      <th>disgust</th>\n",
              "      <th>fear</th>\n",
              "      <th>anger</th>\n",
              "      <th>trust</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A Balcony Over the Fakihani: Three Novellas, 1993</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.10000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Absent (translated by Muhayman Jamil)</td>\n",
              "      <td>0.003774</td>\n",
              "      <td>0.023821</td>\n",
              "      <td>0.007547</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.012500</td>\n",
              "      <td>0.01250</td>\n",
              "      <td>0.012500</td>\n",
              "      <td>0.007547</td>\n",
              "      <td>0.012500</td>\n",
              "      <td>0.049146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algerian White: A Narrative</td>\n",
              "      <td>0.084214</td>\n",
              "      <td>0.053559</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.053206</td>\n",
              "      <td>0.053559</td>\n",
              "      <td>0.038055</td>\n",
              "      <td>0.061311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>American Granddaughter</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.011905</td>\n",
              "      <td>0.011905</td>\n",
              "      <td>0.023810</td>\n",
              "      <td>0.02381</td>\n",
              "      <td>0.023810</td>\n",
              "      <td>0.011905</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.023810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Blue Lorries – 2015</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.05000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "0                                              TITLE  ...     trust\n",
              "0  A Balcony Over the Fakihani: Three Novellas, 1993  ...  0.000000\n",
              "1              Absent (translated by Muhayman Jamil)  ...  0.049146\n",
              "2                        Algerian White: A Narrative  ...  0.061311\n",
              "3                             American Granddaughter  ...  0.023810\n",
              "4                                Blue Lorries – 2015  ...  0.000000\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zFnZEA7avkju",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_review_emtn_agg = reviewer_emotion.groupby(['Book']).mean().reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ChOTDZKBEUIN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f0fb4969-0755-41ee-f7f5-c45e9569d78b"
      },
      "source": [
        "df_review_emtn_agg.head() #user reviews"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Book</th>\n",
              "      <th>sadness</th>\n",
              "      <th>surprise</th>\n",
              "      <th>positive</th>\n",
              "      <th>joy</th>\n",
              "      <th>negative</th>\n",
              "      <th>anticipation</th>\n",
              "      <th>disgust</th>\n",
              "      <th>fear</th>\n",
              "      <th>anger</th>\n",
              "      <th>trust</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A Balcony Over The Fakihani</td>\n",
              "      <td>0.010874</td>\n",
              "      <td>0.017247</td>\n",
              "      <td>0.004426</td>\n",
              "      <td>0.008733</td>\n",
              "      <td>0.023948</td>\n",
              "      <td>0.032600</td>\n",
              "      <td>0.038879</td>\n",
              "      <td>0.021312</td>\n",
              "      <td>0.022286</td>\n",
              "      <td>0.035013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A Sister to Scheherazade</td>\n",
              "      <td>0.015402</td>\n",
              "      <td>0.013375</td>\n",
              "      <td>0.006707</td>\n",
              "      <td>0.009169</td>\n",
              "      <td>0.011481</td>\n",
              "      <td>0.014336</td>\n",
              "      <td>0.018649</td>\n",
              "      <td>0.015512</td>\n",
              "      <td>0.010067</td>\n",
              "      <td>0.036209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A Sky So Close</td>\n",
              "      <td>0.005764</td>\n",
              "      <td>0.014409</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.005764</td>\n",
              "      <td>0.008646</td>\n",
              "      <td>0.005764</td>\n",
              "      <td>0.008646</td>\n",
              "      <td>0.008646</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.020173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A compass for the sunflower</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Absent</td>\n",
              "      <td>0.019713</td>\n",
              "      <td>0.014996</td>\n",
              "      <td>0.008643</td>\n",
              "      <td>0.007759</td>\n",
              "      <td>0.012134</td>\n",
              "      <td>0.013146</td>\n",
              "      <td>0.017055</td>\n",
              "      <td>0.011653</td>\n",
              "      <td>0.007887</td>\n",
              "      <td>0.039666</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           Book   sadness  ...     anger     trust\n",
              "0  A Balcony Over The Fakihani   0.010874  ...  0.022286  0.035013\n",
              "1     A Sister to Scheherazade   0.015402  ...  0.010067  0.036209\n",
              "2               A Sky So Close   0.005764  ...  0.000000  0.020173\n",
              "3   A compass for the sunflower  0.000000  ...  0.000000  0.000000\n",
              "4                       Absent   0.019713  ...  0.007887  0.039666\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vy1d_EkoG-Q9",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "#### **Plotting the NRC Emotions**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0eXYsfZT5yoa",
        "colab_type": "text"
      },
      "source": [
        "Reviewer Emotion Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "visFDgL16Vk5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#select the books\n",
        "array = ['The Blue Aubergine ','Beirut - A Novel','Improvisations on a Missing String','Red Wine ','Points of the Compass: Stories','Leaves of Narcissus',\\\n",
        "'The Inheritance ','The Open Door ','The Man from Bashmour','Daughter of Damascus ','Year of the Elephant ','Fantasia: An Algerian Cavalcade ','Children of the New World']\n",
        "\n",
        "df_revier_emtn_plt = df_review_emtn_agg.loc[df_review_emtn_agg['Book'].isin(array)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-JM0ubIHMIT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Df_reviewr = df_revier_emtn_plt[(df_revier_emtn_plt['sadness']>0)|(df_revier_emtn_plt['surprise']>0)|(df_revier_emtn_plt['positive']>0)|(df_revier_emtn_plt['joy']>0)|\\\n",
        "                 (df_revier_emtn_plt['negative']>0)|(df_revier_emtn_plt['anticipation']>0)|(df_revier_emtn_plt['disgust']>0)|(df_revier_emtn_plt['fear']>0)| \\\n",
        "                  (df_revier_emtn_plt['anger']>0)|(df_revier_emtn_plt['trust']>0)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lC3RqRuuuJRG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Df_reviewr = Df_reviewr[['Book','sadness','surprise','joy','anticipation','disgust','fear','anger','trust']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ji1mKR1RL9MW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_reviewer_heat = Df_reviewr.melt(id_vars=[\"Book\"], \n",
        "         var_name=\"Emotions\", \n",
        "         value_name=\"Emtn_Val\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ClaKiEPFLvt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "f41ccf23-4536-46ec-cc9c-1da3db492403"
      },
      "source": [
        "fig = px.treemap(df_reviewer_heat, path=['Book', 'Emotions'], values='Emtn_Val',\n",
        "                  hover_data=['Emtn_Val'],\n",
        "                  color_continuous_scale='day')\n",
        "\n",
        "fig.update_layout(treemapcolorway = [\"#46B005\", \"#112AA9\", \"#7611A9\", \"#A91197\", \"#A9114B\", \"#B00505\", \"#A8B005\",\"#15CDE6\"])\n",
        "fig.update_layout(\n",
        "    margin = {'t':0, 'l':0, 'r':0, 'b':0}\n",
        ")\n",
        "fig.update_layout(uniformtext=dict(minsize=10, mode='hide'))\n",
        "fig.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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      "source": [
        "Publisher Emotion Analysis"
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      "source": [
        " #select the books\n",
        "array = ['The Blue Aubergine','Beirut - A Novel','Improvisations on a Missing String','Red Wine','Points of the Compass: Stories','Leaves of Narcissus',\\\n",
        "'The Inheritance','The Open Door','The Man from Bashmour','Daughter of Damascus','Year of the Elephant','Fantasia: An Algerian Cavalcade','Children of the New World']\n",
        "\n",
        "df_publisher_emtn_plt = df_agg_emtn.loc[df_agg_emtn['TITLE'].isin(array)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ie7FwV1EK2G2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Df_publisher = df_publisher_emtn_plt[(df_publisher_emtn_plt['sadness']>0)|(df_publisher_emtn_plt['surprise']>0)|(df_publisher_emtn_plt['positive']>0)|(df_publisher_emtn_plt['joy']>0)|\\\n",
        "                 (df_publisher_emtn_plt['negative']>0)|(df_publisher_emtn_plt['anticipation']>0)|(df_publisher_emtn_plt['disgust']>0)|(df_publisher_emtn_plt['fear']>0)| \\\n",
        "                  (df_publisher_emtn_plt['anger']>0)|(df_publisher_emtn_plt['trust']>0)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rzhmKpSP6ndk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Df_publisher = Df_publisher[['TITLE','sadness','surprise','joy','anticipation','disgust','fear','anger','trust']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4IEdv77v6rko",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_publisher_heat = Df_publisher.melt(id_vars=[\"TITLE\"], \n",
        "         var_name=\"Emotions\", \n",
        "         value_name=\"Emtn_Val\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3L1zNDVC6uEG",
        "colab_type": "code",
        "colab": {
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          "height": 542
        },
        "outputId": "305e18ac-623c-47c0-9952-3849270a2ec5"
      },
      "source": [
        "fig = px.treemap(df_publisher_heat, path=['TITLE', 'Emotions'], values='Emtn_Val',\n",
        "                  hover_data=['Emtn_Val'],\n",
        "                  color_continuous_scale='day')\n",
        "\n",
        "fig.update_layout(treemapcolorway = [\"#46B005\", \"#112AA9\", \"#7611A9\", \"#A91197\", \"#A9114B\", \"#B00505\", \"#A8B005\",\"#15CDE6\"])\n",
        "fig.update_layout(\n",
        "    margin = {'t':0, 'l':0, 'r':0, 'b':0}\n",
        ")\n",
        "fig.update_layout(uniformtext=dict(minsize=10, mode='hide'))\n",
        "fig.show()\n"
      ],
      "execution_count": null,
      "outputs": [
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