Sentiment analysis using ML models
I just rewrote parts of my Positive Hacker News RSS Feed project to use an ML model to filter out any negative news from the Hacker News timeline. This method is far more reliable than the previous method of using a rule-based sentiment analyzer through NLTK.
I'm using the model cardiffnlp/twitter-roberta-base-sentiment-latest, which was trained on a huge amount of tweets. It's really tiny (~500 MB) and easily runs inside the existing GitHub Actions workflows. You can try out the model yourself on the HuggingFace model card.
If you want to subscribe to more positive tech news, simply replace your Hacker News feed of your RSS reader with this one (or add it if you haven't already): https://garritfra.github.io/positive_hackernews/feed.xml