Deep Learning Based Fusion Approach for Hate Speech Detection

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Zhou, Yanling, Yang, Yanyan, Liu, Han, Liu, Xiufeng, Savage, Nick
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Bert; Bit error rate; Classification; Classifiers; classifiers fusion; CNN; Coders; Context modeling; Deep learning; Digital media; Feature extraction; Hate speech; Machine learning; Social network services; Task analysis; Voice activity detection
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3009244

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.