Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (23): 211-218.DOI: 10.3778/j.issn.1002-8331.2006-0324

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

Sequential Three-Way Sentiment Classification Combined with Ensemble Learning

WANG Qin, LIU Dun   

  1. School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China
  • Online:2021-12-01 Published:2021-12-02



  1. 西南交通大学 经济管理学院,成都 610031


Sentiment classification has always been an important research hotspot in natural language processing tasks, and is widely used in many scenarios such as e-commerce reviews, hotspot forums and public opinion. How to improve the performance of sentiment classification model is still a key research problem in the field of sentiment analysis. Ensemble learning is an effective method to improve the overall performance of the model by combining several classifiers. Based on the ideas of granular computing and three-way decisions, as well as the advantages of ensemble learning, this paper constructs a multi-granularity sequential three-way decision model combined with ensemble learning. Firstly, it builds a text multi-granularity structure through the N-gram language model to form the basis of sequential three-way sentiment classification. Secondly, three classification algorithms are designed to improve the classification performance at each granularity. Finally, it verifies the model through four datasets. The results demonstrate the proposed method can not only improve the overall classification performance, but also reduce the classification cost.

Key words: sentiment classification, sequential three-way decisions, multi-granularity, ensemble learning



关键词: 情感分类, 序贯三支决策, 多粒度, 集成学习