Pattern VS NLTK

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

if you're curious about the nitty gritty, the parsing module's documentation is well written and doesn't require a comp sci or linguistics degree to get the gist of what's happening.

Spacy. If you have time, explore nltk (the NLTK book is actually a really good place to start). I'm kind of fond of the https://github.com/clips/pattern -- it doesn't get the appreciation it deserves

We can use NLTK, a powerful library for Python that provides easy-to-use interfaces to over 50 corpora and lexical resources.

Using the NTLK Natural Language Toolkit

Is this just using LLM to be cool? How does pure LLM with simple "In the scale between 0-10"" stack up against traditional, battle-tested sentiment analysis tools?

Gemini suggests NLTK and spaCy

https://www.nltk.org/

https://spacy.io/

alternatively, could we not simply split by common characters such as newlines and periods, to split it within sentences? it would be fragile with special handling required for numbers with decimal points and probably various other edge cases, though.

there are also Python libraries meant for natural language parsing[0] that could do that task for us. I even see examples on stack overflow[1] that simply split text into sentences.

[0]: https://www.nltk.org/

Check out https://www.nltk.org/ and work through it, it'll give you a foundational understanding of how all this works, but very basically it's just a fancy auto-complete.

NLTK Documentation

If this is True, then you need also Natural Language Toolkit to process the words.