Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (2): 256-264.doi: 10.11896/jsjkx.201200082

• Artificial Intelligence • Previous Articles     Next Articles

Improved Topic Sentiment Model with Word Embedding Based on Gaussian Distribution

LI Yu-qiang1, ZHANG Wei-jiang1, HUANG Yu1, LI Lin1, LIU Ai-hua2   

  1. 1 School of Computer Science and Technology,Wuhan University of Technology,Wuhan 430063,China
    2 School of Energy and Power Engineering,Wuhan University of Technology,Wuhan 430063,China
  • Received:2020-12-08 Revised:2021-03-13 Online:2022-02-15 Published:2022-02-23
  • About author:LI Yu-qiang,born in 1977,Ph.D,asso-ciate professor,master tutor.His main research interests include machine learning and big data analysis.
    ZHANG Wei-jiang,born in 1994,postgraduate.His main research interests include machine learning and big data analysis.
  • Supported by:
    National Social Science Foundation of China(15BGL048).

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Abstract: In recent years,the topic sentiment model as an important research in the field of unsupervised learning,has been used in text topic mining and sentiment analysis.However,Weibo has brought some challenges to the topic sentiment model because of its short text and in complete structure.Therefore,the related research and improvement work of this paper will be carried out around the topic sentiment model of Weibo.We introduce the word vector technology to the popular model-TSMMF(topic sentiment model based on multi-feature fusion),use multivariate Gaussian distribution to sample neighboring words fast from the word embedding space,and replace the words generated by the Dirichlet multinomial distribution.Thus,the words with lowcooccurrence frequency and less information will be transformed into words with prominent topic and clear information.At the same time,the nearest neighbor search algorithm is used to further improve the running speed of the model when processing large-scale Weibo corpus,and then the GWE-TSMMF model is proposed.The experimental results show that the average F1 value of GWE-TSMMF model is about 0.718.The sentiment polarity analysis is better than the original model and the existing mainstream word embedding topic sentiment models (WS-TSWE and HST-SCW).

Key words: Gaussian distribution, Topic sentiment model, Weibo sentiment polarity analysis, Word embedding

CLC Number: 

  • TP391
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