Sarcasm is common in social media, and people use it to express their opinions with stronger emotions indirectly. Although it belongs to a branch of sentiment analysis, traditional sentiment analysis methods cannot identify the rhetoric of irony as it requires a significant amount of background knowledge. Existing sarcasm detection approaches mainly focus on analyzing the text content of sarcasm using various natural language processing techniques. It is argued herein that the essential issue for detecting sarcasm is examining its context, including sentiments of texts that reply to the target text and user’s expression habit. A dual-channel convolutional neural network is proposed that analyzes not only the semantics of the target text, but also its sentimental context. In addition, SenticNet is used to add common sense to the long short-term memory (LSTM) model. The attention mechanism is then applied to take the user’s expression habits into account. A series of experiments were carried out on several public datasets, the results of which show that the proposed approach can significantly improve the performance of sarcasm detection tasks.
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This work is partially supported by the National Natural Science of Foundation of China (Grant Nos. 61902010 and 61671030), Beijing Excellent Talent Funding-Youth Project (No. 2018000020124G039), and Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education.
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Du, Y., Li, T., Pathan, M.S. et al. An Effective Sarcasm Detection Approach Based on Sentimental Context and Individual Expression Habits. Cogn Comput (2021). https://doi.org/10.1007/s12559-021-09832-x
- Sarcasm detection
- Convolutional neural network
- Attention mechanism
- Sentimental context
- Expression habit