A Novel Retrospective-Reading Model for Detecting Chinese Sarcasm Comments of Online Social Network

Lei Zhang, Xiaoming Zhao, Qinfeng Mao, Yanbing Yang, Dong Li, Haizhou Wang

Abstract: Through the use of sarcastic sentences on social media, people can express their strong emotions. Therefore, the detection of sarcasm in social media has received more and more attention over the past years. Classifying a sentence as sarcastic or nonsarcastic heavily relies on the contextual information of the sentence. However, only focusing on the features of target text is the main solution of most existing research. Moreover, the scale of publicly available Chinese sarcasm dataset is very small and does not contain the contextual information. To address the issues mentioned above, we build a Chinese sarcasm dataset from Bilibili, which is one of the most widely used social network platforms in China and has a significant number of sarcastic comments and contextual information. As far as we know, our dataset is the first publicly available large-scale Chinese sarcasm dataset including contextual information. Additionally, we have proposed a novel retrospective reading method for detecting sarcasm that leverages contextual information to improve model's performance. The experimental results show the effectiveness of the proposed model and the significance of contextual information for Chinese sarcasm detection: achieving the highest F-score of 0.6942, outperforming existing state-of-the-art (SOTA) approaches. The study presented in this article offers approaches and ideas for future Chinese sarcasm detection studies.