Preface

This system is by no means complete, nor has it been organized exactly how I would like for it to be. Having said that, there is enough foundation here that can provide benefit to people beyond myself – hence me sharing it.

How it started

ai is a personal library I wrote originally a while ago (2017), where I wanted to determine the sentiment of sentences. I found this interesting because sentiment is not incredibly straightforward. What I mean by this is that merely saying the word ‘no’ does not make a setence negative. In fact, if I were to say something like ‘no I do not want to swallow fire’, and I meant it in a literal sense, then this would technically be positive, in that it is a good decision for one to not swallow fire.

Anyways I did some research and I found nltk - (Natural Language Toolkit) and it provides simple ways of determining sentiment and polarity (summed sentiment) of chunks of text. Fast-forward to today, this is one of the foundation libraries used in many libraries present on hugging-face, which is helpful for context because it has provided benefit for quite a while, with a wide breadth of implementation. How it works is including a variety of corpora that in some cases have been used for decades, (for example: Brown Corpus), one called vader-sentiment, a submodule of nltk that (as a sidenote) has some funny associations with my given name. Anyways, that specifically handles assigning sentiment in essence through keywords. At a lower-level, this is done by applying scalar operations on matrices and adjusting the overall sentiment as a result, found through the various columns of the keyword matrix, concepts from Discrete Mathematics and Linear Algebra, and Grammar for that matter.

Suffice to say there are some cool concepts included in the libraries that are included through nltk. And again, this started the process as I wanted to determine the sentiment of a given sentence.

What it became

Through experience, with various organizations and some academic work with various universities, I learned how I can call further abstractions of these concepts as a means of doing more full-fledged work within Machine Learning, per se, specifically within concepts like multi-language translation and chatbot technology.

From there I went on and continued incorporating this work within the library, while also providing documentation and appropriate sources for people. Feel free and read the documentation!

Conclusion

Sometimes it is a good idea to start building something, even if it is super simple, given how it could blossom over time.