Deep Learning Based Fusion Approach for Hate Speech Detection

Project Code :TCMAPY310

Objective

The objective of this project is to present the principle of three types of text classification methods, ELMo, BERT and CNN, and applied them to hate speech detection, then the performance is improved by fusion from two perspectives: the fusion of the classification results of ELMo, BERT and CNN, and the fusion of the classification results of three CNN classifiers with different parameters.

Abstract

In recent years, the increasing prevalence of hate speech in social media has been considered as a serious problem worldwide. Many governments and organizations have made significant investment in hate speech detection techniques, which have also attracted the attention of the scientific community. Although plenty of literature focusing on this issue is available, it remains difficult to assess the performances of each proposed method, as each has its own advantages and disadvantages. A general way to improve the overall results of classification by fusing the various classifiers results is a meaningful attempt.

KEYWORDS: Hate Speech, Machine Learning, Bert

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