Enhanced Semantic Representation Learning for Sarcasm Detection by Integrating Context-Aware Attention and Fusion Network
Author:
Hao Shufeng12, Yao Jikun3, Shi Chongyang4, Zhou Yu12, Xu Shuang12, Li Dengao12ORCID, Cheng Yinghan4
Affiliation:
1. College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China 2. Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan 030024, China 3. School of Economics and Management, China University of Petroleum (East China), Qingdao 266580, China 4. School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Abstract
Sarcasm is a sophisticated figurative language that is prevalent on social media platforms. Automatic sarcasm detection is significant for understanding the real sentiment tendencies of users. Traditional approaches mostly focus on content features by using lexicon, n-gram, and pragmatic feature-based models. However, these methods ignore the diverse contextual clues that could provide more evidence of the sarcastic nature of sentences. In this work, we propose a Contextual Sarcasm Detection Model (CSDM) by modeling enhanced semantic representations with user profiling and forum topic information, where context-aware attention and a user-forum fusion network are used to obtain diverse representations from distinct aspects. In particular, we employ a Bi-LSTM encoder with context-aware attention to obtain a refined comment representation by capturing sentence composition information and the corresponding context situations. Then, we employ a user-forum fusion network to obtain the comprehensive context representation by capturing the corresponding sarcastic tendencies of the user and the background knowledge about the comments. Our proposed method achieves values of 0.69, 0.70, and 0.83 in terms of accuracy on the Main balanced, Pol balanced and Pol imbalanced datasets, respectively. The experimental results on a large Reddit corpus, SARC, demonstrate that our proposed method achieves a significant performance improvement over state-of-art textual sarcasm detection methods.
Funder
Fundamental Research Program of Shanxi Province Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province Key Research and Development Program in Shanxi Province National Natural Science Foundation of China Scientific Research Fund Project of Taiyuan University of Technology
Subject
General Physics and Astronomy
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