A tool for identifying the clues in survey research data

Amazon reviews sentiment analysis

Uber reviews Sentimental Analysis using Logistic Regression in Spark Session for faster parallel computation in python.

Under the broad research question: What are the impact of COVID-19 in ones life? Our team collected Twitter data to answer research hypothesis, producing models, and have a better understanding of the factors that influence one's reaction to the global pandemic.

Chrome extension to prevent from distraction on YouTube

Spam classifier for web

End-to-end data pipeline for e-commerce sentiment analysis. Features resilient web scraping with Selenium and NLP processing for BI insights.

This project includes Twitter Social Media Analytics, Facebook Graph Analysis and News API Data Analysis. See the final report for full understanding.

A Sequential neural network model was built to train the movie reviews data set. Training for 20 epochs resulted in the best testing accuracy of 84%. A GUI was built to input and test different reviews.

This repository contains a classifier used for sentiment analysis of product reviews. The training set consists of reviews written by Amazon customers for various food products. The reviews, originally given on a 5 point scale, have been adjusted to a +1 or -1 scale, representing a positive or negative review, respectively.

A simple Flask web application that performs sentiment analysis on user-provided text using the TextBlob library. Users can input phrases through a web form or use the REST API to submit text and receive sentiment analysis results(-1, 0 , +1). Ideal for understanding basic natural language processing and API integration with Flask.