Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220300018-6.doi: 10.11896/jsjkx.220300018

• Artificial Intelligence • Previous Articles     Next Articles

Study on Satire Detection Based on Sentiment-Topic-Satire Hybrid Model

FU Yue1, SHI We2   

  1. 1 School of Economics and Management,Huzhou College,Huzhou,Zhejiang 313000,China;
    2 School of Economics and Management,Zhejiang Dcean University,Zhoushan,Zhejiang 316022,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:FU Yue,born in 1983,master,lecturer.Her main research interests include network public opinion and text mining.SHI Wei,born in 1981, Ph.D, profes-sor.His main research interests include business intelligence and affec affective computing.
  • Supported by:
    This work was supported by General Program of National Social Science Foundation of China(20BXW013).

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Abstract: Satire detection is a subtask of opinion mining.Its main purpose is to identify the opinion or emotions expressed by users in written texts.Satirical sentences in texts often have mixed sentiment polarity.Correctly identifying satirical sentences and non satirical sentences plays an important role in sentiment analysis.Various satire detection methods are based on machine lear-ning classifiers,in which the training of classifiers is mainly based on simple words or dictionary features.The purpose of this studyis to establish an unsupervised probabilistic relationship model to identify satirical themes according to the sentiment distribution of words in microblog.The model estimates the related sentiment based on the topic level distribution,evaluates the sentiment related words appearing in the short text,and gives the sentiment related labels.Experimental results show that the model is superior to other latest baseline models in satire detection,and is very suitable for satire prediction of short text.

Key words: Satire, Sentiment analysis, Opinion mining, Topic model

CLC Number: 

  • TP391.1
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