Journal of East China Normal University(Natural Science) ›› 2023, Vol. 2023 ›› Issue (6): 95-107.doi: 10.3969/j.issn.1000-5641.2023.06.009

• Computer Science • Previous Articles     Next Articles

Integrating multi-granularity semantic features into the Chinese sentiment analysis method

Juxiang REN1(), Zhongbao LIU2,3,*()   

  1. 1. School Information Engineering, Shanxi Vocational University of Engineering Science and Technology, Jinzhong, Shanxi 030619, China
    2. School of Information Science, Beijing Language and Culture University, Beijing 100083, China
    3. School of Software, Quanzhou University of Information Engineering, Quanzhou, Fujian 362000, China
  • Received:2022-06-20 Online:2023-11-25 Published:2023-11-23
  • Contact: Zhongbao LIU;


Chinese sentiment analysis is one of important researches in natural language processing, which aims to discover the sentimental tendencies in the Chinese text. In recent years, research on Chinese text sentiment analysis has made great progress in efficiencies, but few studies have explored the characteristics of the language and downstream task requirements. Therefore, in view of the particularity of Chinese text and the requirements of sentiment analysis, using the Chinese text sentiment analysis method that integrates multi-granularity semantic features, such as characters, words, radicals, and part-of-speech is proposed. This introduces radical features and emotional part-of-speech features based on character and word features. Additionally, this integration uses bidirectional the long short-term memory network (BLSTM), attention mechanism and recurrent convolutional neural network (RCNN). The softmax function is used to predict the sentimental tendencies by integrating multi-granularity semantic features. The comparative experiment results on the NLPECC (natural language processing and Chinese computing) dataset showed that the F1 score of the proposed method was 84.80%, which improved the performance of the existing methods to some extent and completed the Chinese text sentiment analysis task.

Key words: Chinese text, multi-granularity semantic features, sentiment analysis, big data environment

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