Topic Modelling.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Topic Modelling.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKI0Du2L-c8D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d6958f4c-8dcb-4a07-a3bb-018ee240a49b"
      },
      "source": [
        "#connecting to google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmgkIThV-ss7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%cd path\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkCW5jhvicWa",
        "colab_type": "text"
      },
      "source": [
        "#### **Text Cleaning**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFBXVp64_Az_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#cleaning the text and returning a list of tokens\n",
        "import spacy\n",
        "spacy.load('en')\n",
        "from spacy.lang.en import English\n",
        "parser = English()\n",
        "def tokenize(text):\n",
        "    lda_tokens = []\n",
        "    tokens = parser(text)\n",
        "    for token in tokens:\n",
        "        if token.orth_.isspace():\n",
        "            continue\n",
        "        elif token.like_url:\n",
        "            lda_tokens.append('URL') #removing any links in the text\n",
        "        elif token.orth_.startswith('@'):\n",
        "            lda_tokens.append('SCREEN_NAME')\n",
        "        else:\n",
        "            lda_tokens.append(token.lower_) #converting to lowercase\n",
        "    return lda_tokens"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ht8Wm93-_2fi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "84117f5f-cc99-4459-bf79-f5ea0997be0b"
      },
      "source": [
        "#Lemmatization\n",
        "import nltk\n",
        "nltk.download('wordnet')\n",
        "from nltk.corpus import wordnet as wn\n",
        "def get_lemma(word):\n",
        "    lemma = wn.morphy(word)\n",
        "    if lemma is None:\n",
        "        return word\n",
        "    else:\n",
        "        return lemma\n",
        "    \n",
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "def get_lemma2(word):\n",
        "    return WordNetLemmatizer().lemmatize(word)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJ8yvxlhSGAH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "9b82a49c-0527-473d-e7b9-acf9933cbf24"
      },
      "source": [
        "nltk.download('stopwords')\n",
        "en_stop = set(nltk.corpus.stopwords.words('english'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nuSWfn7oSJiw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#removal of stopwords\n",
        "def prepare_text_for_lda(text):\n",
        "    tokens = tokenize(text)\n",
        "    tokens = [token for token in tokens if len(token) > 4]\n",
        "    tokens = [token for token in tokens if token not in en_stop]\n",
        "    tokens = [get_lemma(token) for token in tokens]\n",
        "    return tokens"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N99rVmu7TZFV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "09d4a0f4-454d-4ca7-bb1e-96382b6e6f17"
      },
      "source": [
        "#Preparing data for LDA\n",
        "import random\n",
        "text_data = []\n",
        "with open('Daughter of Damascus _ Alexander Street, a ProQuest Company.txt') as f:\n",
        "    for line in f:\n",
        "        tokens = prepare_text_for_lda(line)\n",
        "        if random.random() > .99:\n",
        "            print(tokens)\n",
        "            text_data.append(tokens)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['moment', 'honest', 'include', 'touch', 'reveal']\n",
            "['siham', 'purpose', 'writing', 'wholly']\n",
            "['schooling', 'receive', 'training', 'telecommunication', 'allow']\n",
            "['syrian', 'revolution']\n",
            "['staris', 'casting', 'aspersion', 'daughter', 'painstakingly']\n",
            "['responsible', 'entertain', 'others', 'indulge']\n",
            "['boldiness', 'important', 'characteristic']\n",
            "['siham', 'venture', 'try', 'authentic', 'picture', 'possible']\n",
            "[]\n",
            "['common', 'soldier', 'perspective', 'siham', 'father', 'relate', 'defeat']\n",
            "['build', 'house', 'conform', 'bodily', 'spiritual', 'need']\n",
            "['siory', 'change', 'repeat', 'middle']\n",
            "[]\n",
            "['damascus']\n",
            "['whore', 'vegetable']\n",
            "['lover', 'become', 'wonderful']\n",
            "['cling', 'genuine', 'order', 'change']\n",
            "['poking', 'boil', 'ihways']\n",
            "['cotton', 'lady', 'bizuriyya']\n",
            "['round', 'third', 'round', 'whole', 'course', 'complete']\n",
            "['semorat']\n",
            "['answer', 'leave', 'bark', 'usclessly']\n",
            "['along', 'outside', 'angry', 'sister', 'really']\n",
            "[]\n",
            "[]\n",
            "[]\n",
            "['provide', 'amusement', 'others', 'would', 'waich', 'group', 'chile']\n",
            "['white', 'white', 'child', 'ofthe', 'singing']\n",
            "['profits']\n",
            "['almost', 'every', 'friday', 'would', 'spend', 'different', 'place', 'invite', 'reaives']\n",
            "['sweet', 'sesame', 'paste']\n",
            "['smile', 'would', 'consider', 'hope', 'obvious', 'conceal', 'walking', 'sleep', 'realize']\n",
            "[]\n",
            "['mother', 'happy']\n",
            "[]\n",
            "['daughter', 'kibbi', 'yoghurt']\n",
            "[]\n",
            "[]\n",
            "[]\n",
            "['disappear', 'behind', 'white', 'curtain', 'veil']\n",
            "['sound', 'play', 'conductor', 'lovely', 'stoke']\n",
            "[]\n",
            "['feather', 'tickle']\n",
            "['would', 'green', 'productive', 'sprinkle']\n",
            "['threads', 'guest', 'enter', 'rooms', 'black', 'malaya']\n",
            "['would']\n",
            "['disturb', 'sleeping']\n",
            "['married', 'sister', 'eldest', 'paternal', 'mather']\n",
            "[]\n",
            "[]\n",
            "['eoough']\n",
            "['mating', 'could', 'approach']\n",
            "['chadija', 'answer', 'brief', 'plan']\n",
            "['young', 'woman', 'running', 'woman', 'party']\n",
            "[]\n",
            "[]\n",
            "['wedding', 'without', 'cream', 'without', 'colored', 'sweet', 'wedding']\n",
            "['jasmine', 'complain']\n",
            "['interrupt', 'thatthe', 'bride', 'young', 'fifteen', 'years']\n",
            "['dothes', 'young', 'open', 'everything', 'brand']\n",
            "['bridegroom', 'clothes', 'wedding', 'clothes']\n",
            "[]\n",
            "[]\n",
            "[]\n",
            "['bride', 'family', 'remain', 'ceremony']\n",
            "[]\n",
            "[]\n",
            "['trough', 'graves', 'ofthe', 'expression', 'isthe']\n",
            "['woman', 'call', 'forthe', 'congratulation']\n",
            "['clapping', 'sweet', 'laughter', 'fill', 'arouse', 'curiosity']\n",
            "['pass', 'mother', 'pour', 'sugar']\n",
            "['jamal', 'pasha', 'murderer', 'rebel', 'marje', 'square']\n",
            "[]\n",
            "['electrical', 'wire', 'donkey', 'going']\n",
            "['popular', 'symphony', 'perform', 'compose']\n",
            "['colloquial', 'accent']\n",
            "[]\n",
            "['seched', 'directly', 'beother', 'house']\n",
            "['mazza', 'pickle', 'olive']\n",
            "['garlic', 'lemon', 'cucumber', 'lettuce', 'start', 'person']\n",
            "['koran', 'shaikh', 'would', 'pillow', 'place', 'sheepskin']\n",
            "[]\n",
            "['lightening', 'damascus']\n",
            "[]\n",
            "[]\n",
            "[]\n",
            "['ibrahim', 'biess', 'hands', 'doctor']\n",
            "[]\n",
            "[]\n",
            "['fahmi', 'doctor']\n",
            "['health', 'sleep', 'cemetery']\n",
            "['doctor', 'laugh', 'court']\n",
            "[]\n",
            "[]\n",
            "['ofthe', 'relative', 'funeral', 'invite', 'present', 'SCREEN_NAME']\n",
            "[]\n",
            "[]\n",
            "['hands', 'sledgehammer', 'break']\n",
            "[]\n",
            "[]\n",
            "['story', 'revea']\n",
            "[]\n",
            "['antar', 'kasim', 'break']\n",
            "['cousin', 'offer', 'marry', 'mafry']\n",
            "[]\n",
            "['velcomed', 'walk', 'recite', 'bible']\n",
            "['storyteller', 'kassim', 'yawn']\n",
            "[]\n",
            "['stint', 'central', 'flank', 'right', 'small', 'rooms']\n",
            "[]\n",
            "[]\n",
            "['something', 'kind', 'bird']\n",
            "[]\n",
            "['saving', 'usually', 'large', 'workshop', 'fifty', 'loom']\n",
            "['older', 'turn', 'consider', 'provider']\n",
            "['military', 'committee', 'enter', 'large']\n",
            "[]\n",
            "[]\n",
            "[]\n",
            "['suddenly', 'general', 'passing', 'begging']\n",
            "['telegraph', 'company', 'reply']\n",
            "[]\n",
            "['pasha', 'office', 'chief', 'staff', 'mahmud', 'shukat']\n",
            "['whole', 'mountain']\n",
            "['continue', 'reach', 'edirne']\n",
            "[]\n",
            "[]\n",
            "['truth', 'catle', 'hindrance', 'camel']\n",
            "[]\n",
            "['collapse', 'abandon', 'desert']\n",
            "['exclaim']\n",
            "['officer', 'order', 'given', 'soldier']\n",
            "['mates', 'inside', 'barb', 'three', 'month']\n",
            "['form', 'battalion', 'hundred', 'office']\n",
            "['mosaiues', 'become', 'night', 'bread', 'front']\n",
            "['rape', 'country', 'grape', 'grow']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IC-VYrLZjFZw",
        "colab_type": "text"
      },
      "source": [
        "We create a dictionary from the data and then convert it to a Bag-of-word corpus and then save the dictionary and corpus for future use.\n",
        "\n",
        "**What is a Bag-of-Word model?**                                        \n",
        "In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order but keeping multiplicity. \n",
        "\n",
        "ex :-                                                                   \n",
        "John likes to watch movies. Mary likes movies too.                       \n",
        "BoW1 = {\"John\":1,\"likes\":2,\"to\":1,\"watch\":1,\"movies\":2,\"Mary\":1,\"too\":1};"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3B2UPYGThoy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "8335dc16-e1a2-4ce4-d338-8bb8097cde76"
      },
      "source": [
        "from gensim import corpora\n",
        "dictionary = corpora.Dictionary(text_data)\n",
        "corpus = [dictionary.doc2bow(text) for text in text_data]\n",
        "\n",
        "import pickle\n",
        "pickle.dump(corpus, open('corpus.pkl', 'wb'))\n",
        "dictionary.save('dictionary.gensim')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:254: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q99QT4B9USNH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "f0c7f12a-1a4b-4d51-b3c8-9c89a4250719"
      },
      "source": [
        "#LDA to find 5 topics in the data\n",
        "\n",
        "import gensim\n",
        "NUM_TOPICS = 5\n",
        "ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics = NUM_TOPICS, id2word=dictionary, passes=15)\n",
        "ldamodel.save('model5.gensim')\n",
        "topics = ldamodel.print_topics(num_words=4)\n",
        "for topic in topics:\n",
        "    print(topic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(0, '0.025*\"would\" + 0.017*\"consider\" + 0.017*\"damascus\" + 0.009*\"sleep\"')\n",
            "(1, '0.015*\"young\" + 0.015*\"white\" + 0.015*\"siham\" + 0.015*\"others\"')\n",
            "(2, '0.018*\"clothes\" + 0.018*\"mother\" + 0.010*\"would\" + 0.010*\"become\"')\n",
            "(3, '0.017*\"large\" + 0.009*\"sweet\" + 0.009*\"hands\" + 0.009*\"ofthe\"')\n",
            "(4, '0.024*\"woman\" + 0.016*\"wedding\" + 0.016*\"doctor\" + 0.016*\"without\"')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:254: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g44eKoWIWorc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "dfb89863-447d-4c84-c22e-f794411ac0f7"
      },
      "source": [
        "#LDA to a new document\n",
        "with open('Fantasia_ An Algerian Cavalcade _ Alexander Street, a ProQuest Company.txt','r') as f:\n",
        "  new_doc = f.read().replace('\\n', '')\n",
        "  new_doc = prepare_text_for_lda(new_doc)\n",
        "  new_doc_bow = dictionary.doc2bow(new_doc)\n",
        "  print(new_doc_bow)\n",
        "  print(ldamodel.get_document_topics(new_doc_bow))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[(1, 14), (2, 25), (3, 6), (4, 17), (5, 1), (8, 35), (9, 11), (10, 21), (11, 1), (13, 1), (14, 7), (17, 1), (18, 55), (22, 5), (23, 26), (24, 1), (26, 1), (27, 6), (28, 1), (29, 7), (30, 13), (31, 18), (32, 8), (33, 3), (34, 6), (35, 93), (37, 8), (38, 52), (40, 6), (41, 1), (42, 70), (43, 7), (44, 1), (45, 12), (46, 21), (47, 13), (49, 1), (50, 2), (52, 35), (53, 8), (55, 6), (57, 25), (62, 1), (63, 11), (64, 5), (65, 12), (66, 24), (67, 12), (68, 22), (70, 5), (72, 38), (74, 23), (75, 1), (76, 16), (77, 8), (78, 54), (81, 15), (82, 4), (84, 88), (85, 72), (86, 84), (87, 1), (88, 27), (90, 13), (91, 6), (92, 72), (93, 4), (94, 2), (95, 64), (97, 31), (98, 1), (100, 6), (101, 4), (102, 6), (103, 6), (105, 17), (106, 23), (107, 12), (108, 14), (109, 7), (110, 103), (113, 38), (114, 3), (115, 17), (116, 22), (120, 43), (121, 2), (124, 14), (126, 2), (127, 10), (128, 17), (129, 11), (131, 5), (132, 1), (133, 3), (134, 5), (135, 10), (136, 19), (138, 6), (140, 16), (141, 110), (143, 7), (145, 2), (146, 4), (147, 14), (148, 250), (149, 75), (152, 24), (153, 52), (154, 3), (156, 21), (157, 11), (158, 5), (159, 10), (160, 69), (161, 1), (163, 25), (164, 12), (165, 1), (166, 8), (167, 8), (168, 42), (169, 33), (170, 15), (171, 1), (172, 3), (174, 21), (176, 11), (177, 5), (179, 6), (180, 16), (181, 7), (182, 17), (183, 2), (184, 1), (187, 1), (188, 2), (189, 6), (190, 1), (191, 1), (193, 34), (194, 1), (195, 4), (196, 6), (197, 1), (199, 5), (202, 1), (205, 6), (208, 2), (209, 1), (210, 1), (211, 7), (212, 28), (214, 1), (219, 6), (220, 36), (221, 1), (223, 1), (224, 2), (225, 3), (226, 15), (227, 4), (228, 3), (229, 16), (230, 17), (231, 15), (234, 31), (237, 27), (239, 6), (240, 15), (242, 3), (244, 13), (250, 27), (251, 8), (253, 3), (255, 11), (256, 5), (257, 4), (258, 1), (262, 3), (264, 14), (266, 6), (267, 1), (268, 25), (269, 3), (270, 41), (271, 7), (272, 30), (274, 18), (276, 6), (278, 9), (279, 35), (280, 26), (282, 21), (283, 1), (286, 6), (287, 10), (288, 4), (289, 7), (290, 5), (291, 20), (292, 44), (293, 3), (294, 5), (295, 1), (296, 37), (297, 51), (298, 4), (299, 15), (300, 27), (301, 6), (302, 33), (304, 92), (305, 19), (306, 1), (307, 10), (308, 1)]\n",
            "[(0, 0.13409), (1, 0.26889622), (2, 0.19966424), (3, 0.14111173), (4, 0.25623778)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9LYLcjDdYYuw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "64023ecd-2de9-4810-e9a5-f0797ca59a88"
      },
      "source": [
        "#LDA to find 3 topics in the data\n",
        "ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics = 3, id2word=dictionary, passes=15)\n",
        "ldamodel.save('model3.gensim')\n",
        "topics = ldamodel.print_topics(num_words=4)\n",
        "for topic in topics:\n",
        "    print(topic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(0, '0.014*\"sweet\" + 0.010*\"wedding\" + 0.010*\"doctor\" + 0.009*\"siham\"')\n",
            "(1, '0.020*\"would\" + 0.011*\"woman\" + 0.011*\"young\" + 0.010*\"white\"')\n",
            "(2, '0.012*\"round\" + 0.012*\"soldier\" + 0.012*\"break\" + 0.007*\"whole\"')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:254: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55iB7tL4ZcrU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "75d30aa2-bd2a-480f-a62c-113673e85970"
      },
      "source": [
        "ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics = 10, id2word=dictionary, passes=15)\n",
        "ldamodel.save('model10.gensim')\n",
        "topics = ldamodel.print_topics(num_words=4)\n",
        "for topic in topics:\n",
        "    print(topic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(0, '0.021*\"order\" + 0.021*\"change\" + 0.021*\"soldier\" + 0.021*\"officer\"')\n",
            "(1, '0.040*\"clothes\" + 0.021*\"daughter\" + 0.021*\"consider\" + 0.021*\"provider\"')\n",
            "(2, '0.038*\"woman\" + 0.026*\"ofthe\" + 0.026*\"young\" + 0.013*\"bride\"')\n",
            "(3, '0.021*\"break\" + 0.021*\"house\" + 0.021*\"answer\" + 0.021*\"antar\"')\n",
            "(4, '0.030*\"mother\" + 0.016*\"office\" + 0.016*\"pasha\" + 0.016*\"bodily\"')\n",
            "(5, '0.048*\"would\" + 0.020*\"place\" + 0.020*\"become\" + 0.020*\"others\"')\n",
            "(6, '0.038*\"wedding\" + 0.038*\"without\" + 0.020*\"sweet\" + 0.020*\"mafry\"')\n",
            "(7, '0.033*\"enter\" + 0.033*\"large\" + 0.017*\"hands\" + 0.017*\"doctor\"')\n",
            "(8, '0.041*\"white\" + 0.028*\"sister\" + 0.015*\"start\" + 0.015*\"lettuce\"')\n",
            "(9, '0.038*\"damascus\" + 0.020*\"doctor\" + 0.020*\"siham\" + 0.020*\"chadija\"')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:254: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQCF5mTzZvKR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "ab2f74f7-e4d7-4677-909f-98af4647e312"
      },
      "source": [
        "!pip install pyldavis"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pyldavis in /usr/local/lib/python3.6/dist-packages (2.1.2)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from pyldavis) (0.16.0)\n",
            "Requirement already satisfied: numpy>=1.9.2 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (1.18.5)\n",
            "Requirement already satisfied: numexpr in /usr/local/lib/python3.6/dist-packages (from pyldavis) (2.7.1)\n",
            "Requirement already satisfied: jinja2>=2.7.2 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (2.11.2)\n",
            "Requirement already satisfied: joblib>=0.8.4 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (0.16.0)\n",
            "Requirement already satisfied: pytest in /usr/local/lib/python3.6/dist-packages (from pyldavis) (3.6.4)\n",
            "Requirement already satisfied: scipy>=0.18.0 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (1.4.1)\n",
            "Requirement already satisfied: funcy in /usr/local/lib/python3.6/dist-packages (from pyldavis) (1.14)\n",
            "Requirement already satisfied: wheel>=0.23.0 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (0.35.1)\n",
            "Requirement already satisfied: pandas>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (1.0.5)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2>=2.7.2->pyldavis) (1.1.1)\n",
            "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (1.9.0)\n",
            "Requirement already satisfied: pluggy<0.8,>=0.5 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (0.7.1)\n",
            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (8.4.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (1.15.0)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (20.1.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (49.6.0)\n",
            "Requirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (1.4.0)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.17.0->pyldavis) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.17.0->pyldavis) (2018.9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfuBnwLnZicq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 915
        },
        "outputId": "2f3c0211-335b-453b-b792-f7ab2cff32d2"
      },
      "source": [
        "dictionary = gensim.corpora.Dictionary.load('dictionary.gensim')\n",
        "corpus = pickle.load(open('corpus.pkl', 'rb'))\n",
        "lda = gensim.models.ldamodel.LdaModel.load('model5.gensim')\n",
        "\n",
        "import pyLDAvis.gensim\n",
        "lda_display = pyLDAvis.gensim.prepare(lda, corpus, dictionary, sort_topics=False)\n",
        "pyLDAvis.display(lda_display)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:254: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el7611405361614495682871991842\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el7611405361614495682871991842_data = {\"mdsDat\": {\"x\": [-0.008459962413820982, -0.1240776954103905, 0.0017543900689955074, 0.04996891225310213, 0.0808143555021139], \"y\": [-0.0666519588249985, 0.05882782020559557, -0.07448796783695388, -0.006938736677348426, 0.08925084313370518], \"topics\": [1, 2, 3, 4, 5], \"cluster\": [1, 1, 1, 1, 1], \"Freq\": [18.65218162536621, 24.18208122253418, 17.425477981567383, 19.608469009399414, 20.13178825378418]}, \"tinfo\": {\"Term\": [\"woman\", \"clothes\", \"mother\", \"consider\", \"damascus\", \"large\", \"wedding\", \"would\", \"without\", \"round\", \"rooms\", \"doctor\", \"siham\", \"others\", \"house\", \"white\", \"young\", \"become\", \"place\", \"change\", \"order\", \"invite\", \"sister\", \"enter\", \"hands\", \"break\", \"whole\", \"sweet\", \"possible\", \"venture\", \"consider\", \"damascus\", \"hope\", \"obvious\", \"spend\", \"different\", \"almost\", \"friday\", \"conceal\", \"reaives\", \"every\", \"smile\", \"walking\", \"realize\", \"square\", \"rebel\", \"murderer\", \"jamal\", \"marje\", \"eldest\", \"married\", \"mather\", \"paternal\", \"plan\", \"chadija\", \"form\", \"genuine\", \"older\", \"offer\", \"cling\", \"would\", \"sleep\", \"place\", \"answer\", \"invite\", \"pasha\", \"sister\", \"change\", \"order\", \"others\", \"house\", \"perspective\", \"father\", \"defeat\", \"common\", \"group\", \"amusement\", \"provide\", \"waich\", \"relate\", \"chile\", \"years\", \"fifteen\", \"staff\", \"spiritual\", \"mahmud\", \"need\", \"conform\", \"bodily\", \"build\", \"shukat\", \"thatthe\", \"chief\", \"interrupt\", \"touch\", \"open\", \"three\", \"mates\", \"month\", \"siham\", \"white\", \"young\", \"daughter\", \"soldier\", \"bride\", \"office\", \"clothes\", \"mother\", \"possible\", \"venture\", \"authentic\", \"try\", \"picture\", \"night\", \"receive\", \"lovely\", \"bread\", \"training\", \"sound\", \"schooling\", \"conductor\", \"mosaiues\", \"allow\", \"stoke\", \"front\", \"play\", \"telecommunication\", \"symphony\", \"bridegroom\", \"pour\", \"pass\", \"passing\", \"compose\", \"popular\", \"productive\", \"begging\", \"general\", \"sprinkle\", \"would\", \"become\", \"large\", \"fifty\", \"saving\", \"person\", \"start\", \"garlic\", \"fill\", \"arouse\", \"laughter\", \"usually\", \"cucumber\", \"pillow\", \"workshop\", \"loom\", \"curiosity\", \"shaikh\", \"lettuce\", \"clapping\", \"lemon\", \"sheepskin\", \"koran\", \"graves\", \"trough\", \"disappear\", \"veil\", \"isthe\", \"expression\", \"behind\", \"curtain\", \"committee\", \"sweet\", \"hands\", \"ofthe\", \"break\", \"woman\", \"without\", \"round\", \"rooms\", \"colored\", \"cream\", \"flank\", \"third\", \"stint\", \"complete\", \"malaya\", \"SCREEN_NAME\", \"course\", \"central\", \"funeral\", \"right\", \"guest\", \"threads\", \"black\", \"present\", \"relative\", \"small\", \"outside\", \"angry\", \"really\", \"running\", \"along\", \"party\", \"congratulation\", 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              "   // already loaded: just create the visualization\n",
              "   !function(LDAvis){\n",
              "       new LDAvis(\"#\" + \"ldavis_el7611405361614495682871991842\", ldavis_el7611405361614495682871991842_data);\n",
              "   }(LDAvis);\n",
              "}else if(typeof define === \"function\" && define.amd){\n",
              "   // require.js is available: use it to load d3/LDAvis\n",
              "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
              "   require([\"d3\"], function(d3){\n",
              "      window.d3 = d3;\n",
              "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
              "        new LDAvis(\"#\" + \"ldavis_el7611405361614495682871991842\", ldavis_el7611405361614495682871991842_data);\n",
              "      });\n",
              "    });\n",
              "}else{\n",
              "    // require.js not available: dynamically load d3 & LDAvis\n",
              "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
              "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
              "                 new LDAvis(\"#\" + \"ldavis_el7611405361614495682871991842\", ldavis_el7611405361614495682871991842_data);\n",
              "            })\n",
              "         });\n",
              "}\n",
              "</script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUCR5LKhZoJ4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 915
        },
        "outputId": "f2737a80-3be2-4dff-9e5f-d88b8dc8dfbe"
      },
      "source": [
        "lda3 = gensim.models.ldamodel.LdaModel.load('model3.gensim')\n",
        "lda_display3 = pyLDAvis.gensim.prepare(lda3, corpus, dictionary, sort_topics=False)\n",
        "pyLDAvis.display(lda_display3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:254: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el7611405361414106162425536640\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el7611405361414106162425536640_data = {\"mdsDat\": {\"x\": [-0.09085679284738488, 0.06380883533839636, 0.02704795750898858], \"y\": [-0.02045190597011127, -0.065596280886115, 0.08604818685622624], \"topics\": [1, 2, 3], \"cluster\": [1, 1, 1], \"Freq\": [39.15913391113281, 33.41667175292969, 27.424192428588867]}, \"tinfo\": {\"Term\": [\"round\", \"soldier\", \"break\", \"sweet\", \"would\", \"sleep\", \"clothes\", \"daughter\", \"damascus\", \"white\", \"without\", \"invite\", \"rooms\", \"perspective\", \"relate\", \"defeat\", \"common\", \"father\", \"third\", \"complete\", \"course\", \"waich\", \"amusement\", \"chile\", \"provide\", \"group\", \"allow\", \"training\", \"schooling\", \"receive\", \"sweet\", \"without\", \"invite\", \"rooms\", \"every\", \"spend\", \"reaives\", \"different\", \"almost\", \"friday\", \"cream\", \"colored\", \"flank\", \"central\", \"fill\", \"lettuce\", \"cucumber\", \"right\", \"clapping\", \"stint\", \"laughter\", 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              "   !function(LDAvis){\n",
              "       new LDAvis(\"#\" + \"ldavis_el7611405361414106162425536640\", ldavis_el7611405361414106162425536640_data);\n",
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              "   // require.js is available: use it to load d3/LDAvis\n",
              "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
              "   require([\"d3\"], function(d3){\n",
              "      window.d3 = d3;\n",
              "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
              "        new LDAvis(\"#\" + \"ldavis_el7611405361414106162425536640\", ldavis_el7611405361414106162425536640_data);\n",
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              "    // require.js not available: dynamically load d3 & LDAvis\n",
              "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
              "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
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            "text/plain": [
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          },
          "metadata": {
            "tags": []
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          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zXfWHSFbcdy5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 915
        },
        "outputId": "5bbab56d-53ec-48f4-8777-410dfb1e35fb"
      },
      "source": [
        "lda10 = gensim.models.ldamodel.LdaModel.load('model10.gensim')\n",
        "lda_display10 = pyLDAvis.gensim.prepare(lda10, corpus, dictionary, sort_topics=False)\n",
        "pyLDAvis.display(lda_display10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:254: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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}