Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (9): 310-318.doi: 10.11896/jsjkx.230600217

• Artificial Intelligence • Previous Articles     Next Articles

CCSD:Topic-oriented Sarcasm Detection

LIU Qilong, LI Bicheng, HUANG Zhiyong   

  1. School of Computer Science and Technology,Huaqiao University,Xiamen,Fujian 361000,China
  • Received:2023-06-29 Revised:2023-10-22 Online:2024-09-15 Published:2024-09-10
  • About author:LIU Qilong,Born in 1999,postgra-duate.His main research interests include natural language processing,network public opinion knowledge graph and graph neural network.
    LI Bicheng,born in 1970,Ph.D,professor,Ph.D supervisor.His main research interests include intelligent information processing,network ideological security,network public opinion monitoring and guidance,and big data analysis and mining.
  • Supported by:
    Joint Fund of Ministry of Education for Equipment Pre-research(8091B022150).

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Abstract: With the development of social media,an increasing number of people express their opinions about hot topics on social platforms,and the utilization of sarcastic expression has severely affected the accuracy of sentiment analysis in social media.Currently,topic-oriented sarcasm detection research does not consider the role of context and common sense knowledge simultaneously,and also ignores the scene of sarcasm recognition under the same topic.This paper proposes a sarcasm detection with context and commonsense(CCSD)approach.Firstly,the model uses the C3KG commonsense knowledge base to generate commonsense text.Then,the target sentence,topic context,and commonsense text are concatenated as the input to the pre-training BERT model.In addition,an attention mechanism is used to focus on important information in the target sentence and commonsense text.Finally,sarcasm detections are realized through gating mechanism and feature fusion.A topic-oriented sarcasm detection dataset is constructed to verify the effectiveness of the proposed model in specific topics.Experimental results show that the proposed model achieves better performance compared to baseline models.

Key words: Sarcasm detection, Topic-oriented sarcasm detection, Context, Common sense knowledge, Attention mechanism

CLC Number: 

  • TP391
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