Stock Market Sentiment Analysis Botnet
Built a botnet for crowdsourced sentiment analysis of stock market data.
This project involved building a botnet to perform crowdsourced sentiment analysis of stock market data from platforms like Twitter and Reddit. The system began by scraping data through APIs, which I studied extensively to design custom scripts for collecting relevant posts and comments. The scraped data was then processed using Natural Language Processing (NLP) techniques to classify social media sentiment into actionable signals, such as “buy” or “sell.”
To fully automate the process, I developed a bot that ran periodically every 30 minutes. The bot fetched the latest data, analyzed it, and published the generated signals to a Telegram channel for real-time updates. Additionally, I evaluated the signals’ accuracy by comparing them against actual stock price movements. This feedback loop provided a way to refine my sentiment analysis model and measure its effectiveness.