Sentiment analysis model using Hugging Face's RoBERTa achieves 94% accuracy in classifying Udemy course reviews as positive, negative, or neutral

Welcome to the Repsonsive Website for GMO repository. This project provides detailed insights into genetically modified organisms (GMOs) and the role of the Genetic Engineering Appraisal Committee (GEAC) in regulating these organisms.

Sentiment Sense is a Python project that combines VADER sentiment analysis with fine-tuned RoBERTa models to predict sentiment scores for textual data. It provides a streamlined way to analyze sentiment across various texts using state-of-the-art natural language processing techniques.

The WhatsApp Chat Analyzer is a tool designed to provide insightful analysis of your WhatsApp conversations. The analyzer generates visualizations like word clouds and offers sentiment analysis using Vader, helping you uncover patterns and sentiments within your chat history.

An API providing sentiment-scored news for PositivePress using the Vader NLP algorithm. A consuming frontend can be found at: https://github.com/tj2904/positive-press

Summary of Assignment Two from the Second semester of the MSc in Data Analytics program. This repository contains the CA2 assignment guidelines from the college and my submission. To see all original commits and progress, please visit the original repository using the link below.

This repository contains a project focused on performing sentiment analysis on the Amazon Fine Food Reviews dataset. The goal is to analyze customer reviews to determine their sentiment, whether positive or negative, and to gain insights into customer opinions to improve product offerings.

This is an nlp, web-scrapping based ML capstone project, that allows users to retrieve datasets of the products listed on flipkart and amazon for the given product. Further, Sentiment Analysis can also be performed on the comments/reviews.

Generating playlists based on how you feel 😉

A Telegram bot that recommends movies based on mood.

VADER Sentiment Analysis Tool with C++. Valence Aware Dictionary and sEntiment Reasoner (VADER) is a lexicon and rule-based sentiment tool designed to measure sentiment of text from social media. Originally written in Python, this is a port to C++.

An extension of VADER Sentiment Analysis. Valence Aware Dictionary and sEntiment Reasoner (VADER) is a lexicon and rule-based sentiment tool designed to measure sentiment of text from social media. DARTH VADER is a tool that utilizes WordNets to learn words that are not contained within the vader lexicon.

This project predicts stock market performance using sentiment analysis of news headline. The sentiment is visualized using Treemap

"Comprehensive Subtheme Sentiment Analysis of Customer Reviews Using Advanced NLP Techniques"

Natural Language Processing Python Project creating a Sentiment Analysis Classifier with NLTK's VADER and Huggingface's Roberta Transformers

Leveraging sentiment analysis and data augmentation to recreate recipe scoring algorithm with sparse data. Used MLPs and Gradient Boosting Regressors to compare regression metrics such as RMSE and MSE between raw data and raw data in conjunction with augmented data.

For this project, machine learning algorithms are used on amazon fine food reviews dataset to analyze if the given review is a positive review or a negative review.

Tarzan is an automated trading bot designed for swing trading penny stocks. It aggregates data from multiple sources, including social media, and financial news providers to place well-timed trades. The bot aims to maximize returns by holding positions for an average period of 1-14 days.

Sentiment analysis is part of the NLP techniques that consists in extracting emotions related to some raw texts.