Hate Speech Classification in Social Media Using Emotional Analysis

In this paper, we examine methods to classify hate speech in social media. We aim to establish lexical baselines for this task by applying classification methods using a dataset annotated for this purpose. As features, our system uses Natural Language Processing (NLP) techniques in order to expand the original dataset with emotional information and provide it for machine learning classification. We obtain results of 80.56% accuracy in hate speech identification, which represents an increase of almost 100% from the original analysis used as a reference.