Reddit Stock Sentiment

No dataset existed for this, so the project starts with a Selenium scraper. It walks old.reddit.com search results for a given subreddit and keyword, pages until it has enough posts, then pulls the comment tree from each one.

Labelling thousands of comments by hand was not realistic. I used GPT-4o-mini to label each comment positive, negative or neutral, and passed it the original post as context. Without that context a comment like "this will age well" is unclassifiable. The run writes progress to disk after every batch, so a failed API call resumes instead of starting over.

Three LSTM variants train on GPT-2 tokenised sequences, 200 tokens with 128-dimensional embeddings. Reddit comments skew positive, so the training set is balanced by undersampling every class down to the size of the smallest one.

A Flask app runs the whole thing. You submit a subreddit, the scrape and inference run on a background thread against a UUID-keyed task registry, and the page polls for the result instead of blocking.