Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230800011-9.doi: 10.11896/jsjkx.230800011

• Artificial Intelligenc • Previous Articles     Next Articles

Construction Method of Domain Sentiment Lexicon Based on Improved TF-IDF and BERT

JIANG Haoda, ZHAO Chunlei, CHEN Han, WANG Chundong   

  1. 1 Key Laboratory of Computer Vision and System of Ministry of Education,Tianjin University of Technology,Tianjin 300384,China
    2 Tianjin Key Laboratory of Intelligent Computing and Novel Software Technology,Tianjin 300384,China
  • Published:2024-06-06
  • About author:JIANG Haoda,born in 1997,master,is a member of CCF(No.I2375G).His main research interests include natural language processing and sentiment analysis.
    ZHAO Chunlei,born in 1979,Ph.D,is a member of CCF(No.18494M).Her main research interests include natural language processing and network information security.
  • Supported by:
    Key Special Project of “Science and Technology Helps Economy 2020” of National Key R&D Program of China(SQ2020YFF0413781,SQ2020YFF0401503).

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Abstract: The construction of a domain sentiment lexicon is the foundation of domain text sentiment analysis.The existing me-thods for constructing domain sentiment lexicon have problems such as high redundancy of selected candidate sentiment words,inaccurate judgment of sentiment polarity,and high domain dependency.In order to improve the domain specificity of selected candidate sentiment words and the accuracy of judging the polarity of domain sentiment words,a domain sentiment lexicon construction method based on improved term frequency-inverse document frequency(TF-IDF) and BERT is proposed.This method improves the TF-IDF algorithm in the phase of selecting domain candidate sentiment words.The latent dirichlet allocation(LDA) algorithm is combined with the improved TF-IDF algorithm to perform domain corrections,improves the domain specificity of the selected candidate sentiment words.In the polarity judgment stage of candidate sentiment words,the semantic orientation pointwise mutual information(SO-PMI) algorithm is combined with BERT.By fine-tuning the BERT classification model using domain sentiment words,the accuracy of judging the sentiment polarity of domain candidate sentiment words is improved.Experiments are conducted on user comment datasets in different domains,and the experimental results show that this method can improve the quality of the constructed domain sentiment lexicon,and the F1 value of the domain sentiment lexicon constructed by this method for text sentiment analysis in the automotive field and mobile phone field reaches 78.02% and 88.35%,respectively.

Key words: Sentiment analysis, Domain sentiment lexicon, Term Frequency-Inverse Document Frequency(TF-IDF), Latent Dirichlet allocation(LDA), Semantic orientation pointwise mutual information(SO-PMI), BERT model

CLC Number: 

  • TP391.1
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