NLTK VS SnowNLP

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.

In that case, you should definitely have a look at Python's nltk library which stands for Natural Language Toolkit. They have a rich corpus collection for all kinds of specialized things like grammars, taggers, chunkers, etc.

If not, start with Python's Natural Language Toolkit.

Try https://www.nltk.org it runs you through the basics. The book is here

In terms of programming languages, Python is a great first programming language. the learnpython subreddit has lots of good recommendations for resources to get started. Once you're comfortable with the language, NLTK would be a good place to start, and the docs have heaps of examples. Check it out https://www.nltk.org/

The most popular library to do this is NLTK though I believe you can use some of the popular AI API services today as well. Bloomberg launched one.

I've found SnowNLP, but most of the library is over six years old, which is not ideal for long-term projects as it's outside the support window. SnowNLP's methodology for training sentiment data also sounds messy and unreliable. I couldn't find anything else.