Deep Learning Based Fusion Approach for Hate Speech Detection

IEEE Access

Han Liu¹  Xiufeng Liu²  Yanling Zhou³  Nick Savage⁴  Yanyan Yang⁴ 

[1] College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China;Department of Management Engineering, Technical University of Denmark, Kongens Lyngby, Denmark;School of Computer Science and Information Engineering, Hubei University, Hubei, China;School of Computing, University of Portsmouth, Portsmouth, U.K.;

关键词: Hate speech;    machine learning;    Bert;    CNN;    classifiers fusion;   

DOI  :  10.1109/ACCESS.2020.3009244

来源: DOAJ

【 摘 要 】

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. We first focus on several famous machine learning methods for text classification such as Embeddings from Language Models (ELMo), Bidirectional Encoder Representation from Transformers (BERT) and Convolutional Neural Network (CNN), and apply these methods to the data sets of the SemEval 2019 Task 5. We then adopt some fusion strategies to combine the classifiers to improve the overall classification performance. The results show that the accuracy and F1-score of the classification are significantly improved.

【 授权许可】

Unknown