This Jupyter notebook provides a step-by-step guide on how to implement a Naive Bayes classifier from scratch to identify the toxicity in tweets.
The field of social media has grown rapidly, with Twitter playing a crucial role as a platform for sharing thoughts, news, and events. However, offensive or harmful content permeates through Twitter, highlighting the need for efficient and effective toxicity detection mechanisms.
This Jupyter notebook aims to implement a Naive Bayes classifier from scratch and use it to detect toxic tweets. Naive Bayes is a simple and powerful algorithm for predictive modeling and text classification which is robust to irrelevant features. We'll apply this model to a dataset of tweets to classify them as either toxic or non-toxic.