TweeterPy is a python library to extract data from Twitter. TweeterPy API lets you scrape data from a user's profile like username, userid, bio, followers/followings list, profile media, tweets, etc.

Twitter Trends is a web-based application that automatically detects and analyzes emerging topics in real time through hashtags and user mentions in tweets. Twitter being the major microblogging service is a reliable source for trends detection. The project involved extracting live streaming tweets, processing them to find top hashtags and user …

🐦 Put your tweets/likes in an EPUB and delete them like a boss

A tool that continuously scrapes TweetDeck tweets

Downloaded tweets from the most popular news agencies and extract keywords from them. In the next steps, I plotted a word cloud and did a sentiment analysis for tweets that have the keywords.

A cli tool to get old tweets as csv

Different tweet crawlers using tweepy

Python Notebooks for Collecting Tweets and Analyze their text using various text classification and clustering techniques

some handy scripts for scrapping data

Raw dataset having over 16000 tweets (including both sarcastic and non-sarcastic) for researchers aspiring to work on Sarcasm Detection in Hindi.

Scrape tweets without authentication using Selenium WebDriver

Twitter Data Extractor

Leon's Twitter Timeline

Hydra AI: Geospatial Water Management System utilizing AI and NLP for real-time monitoring and optimized resource allocation

#100Devs Twitter Timeline

Bitcoin price predictor and forecasting using sentiment analysis and LSTM

2024 Twitter Automation Bot Scraper | Automate the extraction of tweet data from Twitter using Python and Selenium. This project logs into Twitter, navigates to a profile, scrolls through the page, and collects tweets, including text, dates, links, and images.

This project focuses on analyzing tweets from Twitter using topic modeling techniques and interactive visualizations. It employs Latent Dirichlet Allocation (LDA) to discover topics within the tweet data and generates interactive word clouds based on topic-term strengths derived from the model. Users can explore topics and related tweets interactiv

This project is about analyzing the raw tweet and extracting the text or phrase that best supports the sentiment and categorize tweet into Positive, Negative or Neutral sentiments.

Specific-Aspect Summarization on News According to Social Sentiments on Twitter