Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (17): 196-202.DOI: 10.3778/j.issn.1002-8331.2005-0341

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Comparative Text Classification Method Based on Topic and Keyword Feature

DING Yong, CHENG Jiaqiao, JIANG Cuiqing, WANG Zhao   

  1. 1.School of Management, Hefei University of Technology, Hefei 230009, China
    2.Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education, Hefei 230009, China
  • Online:2021-09-01 Published:2021-08-30



  1. 1.合肥工业大学 管理学院,合肥 230009
    2.过程优化与智能决策教育部重点实验室,合肥 230009


Comparative text is very important for competitive products analysis, but there are few researches on the classification of comparative text in the Q&A field. Aiming at the characteristics of rich information and concentrated topics in Q&A texts, this paper proposes a comparative text classification method based on topic feature and keyword feature expansion. Based on the pretrained topic model, the topic probability distribution of the Q&A text is inferred as its topic feature. In view of the keyword information loss caused by vector concatenation and summation, GRU-autoencoder is designed to realize feature extraction, and the encoder output is used as the keyword feature of Q&A text. Integrating the topic information and keyword semantics, the comparative text features are constructed from the perspectives of linguistics, product, sentiment, social, topic and keyword, then the Q&A text is classified by using various classifiers. The experimental results show that the constructed features are effective and the effect of the classification are better.

Key words: topic model, autoencoder, feature expansion, comparative text classification



关键词: 主题模型, 自编码器, 特征扩展, 比较文本分类