Naive Bayes Classifier in JS, empowering telegram webapp

Today I made a simple implementation of a naive bayes classifier in Javascript. The implementation was largely inspired by this article.

After that, I spent some time to integrate it with this Telegram webapp, and now I have a telegram webapp in which you can mark messages as ‘spam’ or ‘important’, and every message is then classified giving some confidence about the possibility of it of being part of these categories.

It saves the results to the chrome local storage, so that you can train the classifier over time, preserving the results beetween sessions.

When I will train it enough, and if the results are good, I’m looking forward to implement the auto-hiding of messages based on this data. This is intended for large telegram group chats, in which often off-topic messages will just make painful extracting important messages from the enormous amount of things people texted. How many times you look at your preferred messaging app and you find ~50 unread messages? This is meant to resolve this, even if the bayes classifier is not perfect for this task, it was easy to implement and usually gets good results. I’ll update here if I have encouraging result from this test.

Meanwhile, here’s a screenshot: