Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220900137-7.doi: 10.11896/jsjkx.220900137

• Big Data & Data Science • Previous Articles     Next Articles

Review on Methods and Applications of Text Fine-grained Emotion Recognition

WANG Xiya, ZHANG Ning, CHENG Xin   

  1. School of Business,Qingdao University,Qingdao,Shandong 266000,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:WANG Xiya,born in 1997,postgra-duate.Her main research interests include emotion analysis and online public opinion management. ZHANG Ning,born in 1980,Ph.D,professor.His main research interests include business data analysis and online public opinion management.
  • Supported by:
    Social Science Planning Project of Shandong Province,China(18CHLJ22).

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Abstract: Emotional information contained in massive texts on the Internet expresses public views and attitudes.How to identify and utilize emotional resources has become the focus of research in various fields.By combing the relevant theories and literature on fine-grained emotion recognition,this paper summarizes the classification methods and application scenarios,and discusses the technical challenges and practical gaps.Through analysis,it is found that fine-grained emotion recognition methods mainly include emotion lexicon,traditional machine learning and neural network learning,which are mostly used in business analysis and public opinion management.In view of the future research trend,firstly,the real-time updating of online emotion words,domain lexicon construction and semantic analysis technology can be studied.Secondly,how to improve the automatic classification of training data and build a semi-supervised learning model need to be further discussed.In addition,the research of business analysis and public opinion management can explore the integration of aspect extraction and emotion recognition.This paper summarizes and comments on emotion recognition technology and its application,which can provide a reference for the subsequent research.

Key words: Fine-grained emotion recognition, Emotion classification, Emotion lexicon, Machine learning, Neural network learning

CLC Number: 

  • TP391
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