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If anything, this tool tracks with my general opinion on sentiment analysis: it would be awesome if it actually worked, but most algorithms just predict everything as neutral.
For example if you search for bitwarden it ranks three comments as negative, all others as neutral. If I as a human look at actual comments about bitwarden [1] there are lots of comments about people using it and recommending it. As a human I would rate the sentiment as very positive, with some "negative" comments in between (that are really about specific situations where it's the wrong tool).
I've had some positive experiences with using LLMs for sentiment analysis. An LLM can understand context and determine that in the given context "Bitwarden is the answer" is a glowing recommendation, not a neutral statement. But doing sentiment analysis that way eats a lot of resources, so I can't fault this tool for going with the more established approach.
1: https://hn.algolia.com/?dateRange=pastMonth&page=2&prefix=tr...
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CodeRabbit
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Love the fact I didn't need to signup to get value out of this!
Note: I would suggest just removing dark mode for now. Works WAY better in light mode. I almost missed the light mode, and that would have been too bad.
Here's my user test: https://news.pub/?try=https://www.youtube.com/embed/2eac5XZe...
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Pattern
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
There’s some old work [1] that conceptualized sentiment as an interplay between subjectivity and sentiment. The more subjective a statement, the more “range” sentiment gets. I think this is what you are getting at.
I don’t think it ever gained traction, probably because people aren’t interested in creating an actual theory of sentiment that matches the real world.
[1]: https://github.com/clips/pattern/wiki/pattern-en#sentiment