Leveraging 21,000+ Amazon Reviews to conduct Natural Language Processing (NLP), Sentiment Analysis & Supervised Machine Learning to select the best specialty ice cream flavor for our expansion.

Dockerized pipeline that collects tweets from Twitter related to a chosen topic and stores them in MongoDB, analyzes the sentiment of each tweet, loads analyzed tweets in a PostgresDB and posts tweets with sentiment score binned as positive, neutral or negative to a slack channel.

A Python package for extracting posts from reddit (r/wallstreetbets) and running through the vader sentiment analyser.

End-to-End Conditional Poetry Generation

Collection of notebooks written while learning to work with the Twitter API

Currently learning NLP

[NLP, VADER] Project 3 for General Assembly's Data Science Immersive Course. This project builds on the VADER model to create a sentiment analysis model for YouTube comments.

This project classifies Amazon Food comments into positive, neutral, or negative sentiments. It employs two methods: a Bag-of-Words approach using VADER and transformers encoder-decoder approach using T5.

Dogecoin Percentage Change Prediction based on Elon Musk's Tweets (Sentiment Score)

Sentiment analysis on three different datasets using Lexicon and Rule-based sentiment analysis tools

Review Network Vizualization

Krishi Mart (farmer's portal) - an academic project built with ReactJS. Authentication and storage is implemented using Firebase. The review system is enhanced by using Vader Sentimental Analysis library, to display most appreciated products to the users.

The main purpose of this project is to measure the public response to the COVID-19 vaccine with sentiment analysis. Though the vaccine has provided a new hope it has also resulted in several anti-vaccine movements In order to analyze the public opinions and emotions related to the vaccine during the pandemic, I will be utilizing recent Twitter s…

Implementing text mining for sentiment analysis of Indonesian public opinion on Twitter using Naive Bayes and Support Vector Machine (SVM) text classification.

This is the Final Year Project based on Machine Learning Concepts. This project was developed to analyze the sentiments of a given statement.

I used Catboost for training a model on the numerical features of every YouTube video (e.g., the number of views, comments, likes, etc.) along with sentiment analysis of the video descriptions and comments using the VADER sentiment analysis model.

In this repository I will be doing some sentiment analysis in python using two different techniques: VADER (Valence Aware Dictionary and sEntiment Reasoner) - Bag of words approach Roberta pre-trained Model from Huggingface Pipeline

Analyze your text on a positive, neutral or negative (Sentiment analysis)