An off-the-shelf pre-trained Tweet NLP Toolkit (NER, tokenization, lemmatization, POS tagging, dependency parsing) + Tweebank-NER dataset

Hashformers is a framework for hashtag segmentation with Transformers and Large Language Models (LLMs).

Like and retweet your tweets, or search tweets by topic. It stores and serves data with a Flask webapp. 🐦 Live demo running on twitter.com/ai_testing

Stock price prediction using Bidirectional LSTM and sentiment analysis

Blazing fast topic modelling for short texts.

FinABSA is a T5-Large model trained for Aspect-Based Sentiment Analysis specifically for financial domains.

Python package to clean raw tweets for ML applications.

Sentiment Analysis of tweets written in underused Slavic languages (Serbian, Bosnian and Croatian) using pretrained multilingual RoBERTa based model XLM-R on 2 different datasets.

Implementation of an ETL process for real-time sentiment analysis of tweets with Docker, Apache Kafka, Spark Streaming, MongoDB and Delta Lake

How Will Your Tweet Be Received? Predicting theSentiment Polarity of Tweet Replies

Music for your Mood! Tweet at us and we got you covered!

This is a web-app which employs the citizen science program by tracking their tweets and subsequent data analysis on Google Earth Engine, aimed at effective CyanoHAB detection and monitoring in water bodies around the world.

twig.py - a twitter web3 influencer truffle pig used for finding engaged users

Sentiment Analysis on Loksabha Elections 2019

Dexter is a friendly bot powered by a multichannel convolutional neural network 🤖

Tweet Text Writer Recognition Application

web app brand analysis and visualization. built with ReactJs and NodeJS

This project takes on the goal to improve upon Yachay.ai's infrastructure to train a deep learning model to predict coordinates of individual texts.

Performing sentiment analysis on tweets to recognize depression in tweets using NLP methods

CoviSA is a Visualization Dashboard for analyzing the Sentiments and Emotions related to COVID-19 Tweets.The project fetched data from Twitter and analysed it using a ML Model. The web app is developed with ReactJS served over a Flask server.