Acta Scientiarum Naturalium Universitatis Pekinensis ›› 2019, Vol. 55 ›› Issue (1): 105-112.DOI: 10.13209/j.0479-8023.2018.066

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Research on Sentiment Analysis Based on Representation Learning

LI Xiaojun, SHI Hanxiao, CHEN Nannan, LIU Hong, ZOU Yi   

  1. School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018
  • Received:2018-04-15 Revised:2018-08-19 Online:2019-01-20 Published:2019-01-20
  • Contact: SHI Hanxiao, E-mail: hxshory(at)


厉小军, 施寒潇, 陈南南, 柳虹, 邹轶   

  1. 浙江工商大学管理工程与电子商务学院, 杭州 310018
  • 通讯作者: 施寒潇, E-mail: hxshory(at)
  • 基金资助:


The authors propose C&W-SP model — a text sentiment analysis model based on the representation learning. Firstly, an improved training model based on C&W model is proposed which can integrate emotional information and part of speech information in the training process of word embedding. The evaluation of data sets of NLP&CC’2013 is used to compare experimental results with different models. The experimental results show that the C&W-SP model which combines emotion information and part of speech information has the best performance and confirm the effectiveness of the proposed method.

Key words: sentiment analysis, representation learning, deep learning, word embedding


提出一个基于表示学习的文本情感分析模型C&W-SP。首先基于C&W模型的词表示改进训练模型, 实现在词表示训练过程中融入情感信息和词性信息的不同模型设计; 然后利用NLP&CC’2013中的评测数据集, 进行多种模型的实验对比。实验结果表明, 融入情感信息和词性信息的C&W-SP模型性能效果最优, 验证了所提方法的有效性。

关键词: 情感分析, 表示学习, 深度学习, 词表示