leetcode-compensation VS NLTK

- Show some ❤️ if you find it useful - https://github.com/urk-stav/leetcode-compensation

At the core of many AI-powered applications are foundational models—large language models (LLMs) and APIs that provide the intelligence for features like natural language processing, image recognition, and decision-making. These tools serve as the brain of the app, processing inputs and generating outputs that feel intuitive and human-like.

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