Sarcasm detection method based on fusion of text semantics and social behavior information

Abstract

Sarcasm is a complex implicit emotion that poses a significant challenge in sentiment analysis, particularly in social network sentiment analysis.Effective sarcasm detection holds immense practical significance in the analysis of network public opinion.The contradictory nature of sarcastic texts, which exhibit implicit semantics opposite to the real emotions of users, often leads to misclassification by traditional sentiment analysis methods.Moreover, sarcasm in daily communication is often conveyed through non-textual cues such as intonation and demeanor.Consequently, sarcasm detection methods solely relying on text semantics fail to incorporate non-textual information, thereby limiting their effectiveness.To leverage the power of text semantics and social behavior information, a sarcasm text detection method based on heterogeneous graph information fusion was proposed.The approach involved the construction of a heterogeneous information network encompassing users, texts, and emotional words.A graph neural network model was then designed to handle the representations of the heterogeneous graph.The model employed a dual-channel attention mechanism to extract social behavior information, captured the deep semantics of text through emotional subgraphs, and ultimately combined text semantics and social behavior information.Extensive experiments conducted on the Twitter dataset demonstrate the superiority of the proposed method over existing approaches for sarcasm text detection and classification.

Keywords

• sarcasm detection;graph neural network;heterogeneous information fusion;implicit sentiment analysis