Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (7): 12-16.doi: 10.16180/j.cnki.issn1007-7820.2020.07.003

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Research of Emotional Analysis Based on LDA Topic Model

LIU Yanwen,WEI Yun   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 20009,China
  • Received:2019-04-22 Online:2020-07-15 Published:2020-07-15
  • Supported by:
    National Natural Science Foundation of China(1170277);National Natural Science Foundation of China(61472256);Shanghai Science and Technology Commission Scientific Research Project(16111107502)


LDA topic model lacks understanding of word association and related word pairs when extracting features, which affects the precision of emotional polarity classification. Aiming at this problem, this paper proposed a new model to introduce the feature-opinion pair extraction method in the LDA topic model to improve the extraction effect of the feature opinion pairs. Dependency parsing was used to design feature affective word pairs recognition methods of characteristic affective word pairs. Then the recognition method was introduced as a constraint condition into the LDA model to extract the feature sentiment word pairs. The parameters were calculated by Gibbs sampling, and the generation process of the model was proposed. Finally, the emotional polarity of the text was classified using the random forest classification method. In order to verify the validity of the proposed model, the experiment was carried out together with the other two models. When the number of subject was 20,the results showed that the precision, recall and F-Measure were 81.54%、83.13% and 82.33%, which were significantly higher than the other two models.

Key words: product reviews, sentiment analysis, dependency syntax, feature extraction, LDA topic model, random forest algorithm

CLC Number: 

  • TP391.1