The purpose of this project is to track and analyze sentiment over time on the popular stock market discussion forum, r/wallstreetbets (WSB). Starting in April 2025, the market was rocked by huge daily swings (±5%) as tariff uncertainty drove intense volatility — so I wanted to see how the mood on WSB shifted alongside it.
First, I built a pipeline that pulls Reddit comments daily, utilizing Reddit’s public API. To get training data, I ran a batch of 500 comments through OpenAI’s GPT-4-turbo API, asking it to label each one as bullish, bearish, or neutral.
With that labeled sample, I then trained a custom sentiment classifier on top of the twitter-roberta model — a variant of Google’s BERT architecture fine-tuned to handle messy, informal social media text.
I trained this in Google Colab and deployed the finished model to Hugging Face. Now, a second daily workflow runs inference using that model, tagging the latest comments with sentiment labels.
All the code is fully versioned and available on GitHub.
This dashboard shows distribution of bullish and bearish comments over the last 24 hours. To determine comment score, the number of upvotes for bullish and or bearish comments are summed and divided by the total number of upvotes. Nuetral / unrelated comments are ignored for the sake of analysis.
This dashboard highlights the trends in bullish comments over time, and compares it to daily closing prices of the S&P 500 and the Nasdaq 100 Index. Both of these indices are heavy on growth / tech stocks, and generally will reflect the trends in the market.
This dashboard highlights the trends in bearish comments over time, and compares it to daily closing prices of 7-10 Year treasuries and Gold, which are associated with bearish sentiment as they are often used as hedges/ insurance for market downturns.