Classification of Toxicity in Comments using NLP and LSTM
Anusha Garlapati, Neeraj Malisetty, Gayathri Narayanan
Abstract
With the increased usage of online social media platforms, there has been a sharp hike in toxic comments. Toxicity must be reduced. Classification of toxicity in comments has been an effective research field with various newly proposed approaches. This research and analysis provide a novel usage of the Natural Language Processing approach to classify the type of toxicity in comments. This analysis intends to interpret the type of comment and determine the various types of toxic classes such as obscene, identity hate, threat, toxic, insult, severe toxic. The input to our algorithm is comments from online platforms like toxic or non-toxic. Our model aims to predict the toxicity class. This project intends to analyze in phases. In Phase I, the objective is to evaluate the toxicity in comments by giving data through various techniques like TDIDF, spacy that helps data to perceive how every word in a comment is classified into a particular category of toxic class. Here, Algorithm will take comments from test data and predict the type of toxicity for test data like a toxic, threat, and so on. In Phase II, Data is analyzed to organize the comments into toxic and non-toxic categories. This promotes us to perceive the particular comment is toxic or not.