Journal of shanghai Jiaotong University (Science) ›› 2015, Vol. 20 ›› Issue (1): 1-6.doi: 10.1007/s12204-015-1579-x

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Sentiment Analysis for Chinese Text Based on Emotion Degree Lexicon and Cognitive Theories

Sentiment Analysis for Chinese Text Based on Emotion Degree Lexicon and Cognitive Theories

WU Xing* (武 星), Lü Hai-tao (吕海涛), ZHUO Shao-jian (卓少剑)   

  1. (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)
  2. (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)
  • Online:2015-02-28 Published:2015-03-10
  • Contact: WU Xing (武 星) E-mail:xingwuvip@aliyun.com

Abstract:

The mass data of social media and social networks generated by users play an important role in tracking users'sentiments and opinions online. A good polarity lexicon which can effectively improve the classification results of sentiment analysis is indispensable to analyze the user’s sentiments. Inspired by social cognitive theories, we combine basic emotion value lexicon and social evidence lexicon to improve traditional polarity lexicon. The proposed method obtains significant improvement in Chinese text sentiment analysis by using the proposed lexicon and new syntactic analysis method.

Key words:

Chinese text| sentiment analysis| emotion lexicon| social cognitive theory| emotion tendency

摘要:

The mass data of social media and social networks generated by users play an important role in tracking users'sentiments and opinions online. A good polarity lexicon which can effectively improve the classification results of sentiment analysis is indispensable to analyze the user’s sentiments. Inspired by social cognitive theories, we combine basic emotion value lexicon and social evidence lexicon to improve traditional polarity lexicon. The proposed method obtains significant improvement in Chinese text sentiment analysis by using the proposed lexicon and new syntactic analysis method.

关键词:

Chinese text| sentiment analysis| emotion lexicon| social cognitive theory| emotion tendency

CLC Number: