Computer Science ›› 2020, Vol. 47 ›› Issue (6): 194-200.doi: 10.11896/jsjkx.200200127

• Artificial Intelligence • Previous Articles     Next Articles

Review of Comment-oriented Aspect-based Sentiment Analysis

ZHANG Yan, LI Tian-rui   

  1. School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China
  • Received:2020-02-28 Online:2020-06-15 Published:2020-06-10
  • About author:ZHANG Yan,born in 1996,postgra-duate.His main research interests include sentiment analysis and natural language processing.
    LI Tian-rui,born in 1969,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include big data intelligence,rough sets,and granular computing.
  • Supported by:
    This work was supported by the National Key R&D Program of China (2017YFB1401400)

Abstract: Comment-oriented aspect-level sentiment analysis is one of the key issues in text analysis.With the rapid development of social media,the number of online comments has exploded.More and more people are willing to express their attitudes and emotions on the Internet,but the style and quality of online comments are uneven.How to extract the user’s perspective accurately has become a difficulty.At the same time,users also pay more attention to some fine-grained information when browsing comments,and performing aspect-level sentimentanalysis on comments can help users make decisions better.This paper first introduces the related concepts and problem descriptions of aspect-level sentimentanalysis,and then introduces the research status of aspect-level sentiment analysis at home and abroad in recent years from aspects of aspect extraction and aspect-based sentiment analysis.The corpus and sentiment dictionary resources related to the aspect-level sentiment analysis task are shared,and finally the challenges faced by the aspect-level sentiment analysis and the possible future research directions are analyzed.

Key words: Aspect extraction, Internet reviews, Point of view, Sentiment analysis

CLC Number: 

  • TP391
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