Exploring the effectiveness of TFIDF vectorization and Sentiment Analysis in fake news detection using various ML and visualization methods.

User-Driven Product Analysis with Web Scraping & Multi-modal NLP: Sentiment Analysis, Feature Extraction, and Recommendation using Amazon Reviews

Text Summarization using TF-IDF technique in Python.

Sentiment analysis using machine learning classifiers SVM and MLP to investigate potential gender biases in the provided dataset.

Online Lecture Summarizer and Terms and Conditions feature Extractor, with text analytics. A project which demonstrates the applications of relatively new and upcoming fields of NLP- Video Transcript Summarizing and Feature Extraction. In the process, creating a very useful utility for students and for consumers/investors.

Text Sentiment Classification model

Sentiment Analysis on IMDB movie reviews dataset on kaggle using TFIDF technique

Twitter Sentiment Analysis

This webpage finds you a desired cp question from leetcode using provided keywords. The backend is in flask and python. Uses TF-IDF algorithm.

Predicting the success of Kickstarter Projects. 💲

The repository is a duplicate of the local folder which contains codes created by Yuanzhan Gao ([email protected]) to conduct scaled fuzzy matching procedure on EIDL and PPP dataset. Please see the README file for more information.

Final project for CS4300 Information Retrieval System

The project utilizes the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm. The main objective of this project is to measure the similarity between text documents using the TF-IDF algorithm.

Document Search Engine project with TF-IDF abd Google universal sentence encoder model

I analyzed Spider-Man Movie reviews from IMDb. I employed basic NLP techniques like TF-IDF, Sentiment Analysis and Topic Modelling and I shared the results with solid visualizations. All done with R.

Recommends Anime using Content based filtering (using TFIDF vectorization and sigmoid kernel) and collaborative filtering (using KNN)

Determining the class of cancer-causing mutations using text and genetic data

This project aims to answer the question of the common features of successful social enterprises by applying unsupervised learning on 5,210 B corporations impact data.

Reuters 1987 Corpus Topic classification

Text classification