Da-Sol Park  Jeong-Won Cha

10.5626/JOK.2018.45.11.1185

hate-speech fuzzy category representation Semi-supervised learning Natural Language Processing Machine Learning

The number of people embracing damage caused by hate speech on the SNS(Social Network Service) is increasing rapidly. In this paper, we propose a detection method using Semi-supervised learning and Deep Neural Network from a large file to determine whether implied meaning of sentence beyond hate speech detection through comparison with a simple dictionary in twitter sentence is abusive or not. Most of the methods judge the hate speech sentence by comparing with a blacklist comprising of hate speech words. However, the reported methods have a disadvantage that skillful and subtle expression of hate speech cannot be identified. So, we created a corpus with a label on whether or not to hate speech on Korean twitter sentence. The training corpus in twitter comprised of 44,000 sentences and the test corpus comprised of 13,082 sentences. The system performance about the explicit abusive sentences of the F1 score was 86.13% on the model using 1-layer syllable CNN and sequence vector. And the system performance about the implicit abusive sentences of the F1 score 25.53% on the model using 1-layer syllable CNN and 2-layer syllable CNN and sequence vector. The proposed method can be used as a method for detecting cyber-bullying.