Offensive Language Recognition in Social Media

Abstract

This article proposes an approach to solving the problem of multiclassification within the framework of aggressive language recognition in Twitter. At the stage of preprocessing external data is added to the existing dataset, which is based on information in the links in dataset. This made it possible to expand the training dataset and there by to improve the quality of the classification. The model created is an ensemble of classical machine learning models included Logistic Regression, Support Vector Machines, Naive Bayes models and a combination of Logistic Regression and Naive Bayes. The obtained value of macro F1-score for one of the experiments achieved 0.61, which exceeds the state-of-art published value by 1 percentage point. This indicates the potential value of the proposed approach in the field of hate speech recognition in social media.