A Fuzzy Computing Model for Identifying Polarity of Chinese Sentiment Words.

Journal: Computational intelligence and neuroscience
Published Date:

Abstract

With the spurt of online user-generated contents on web, sentiment analysis has become a very active research issue in data mining and natural language processing. As the most important indicator of sentiment, sentiment words which convey positive and negative polarity are quite instrumental for sentiment analysis. However, most of the existing methods for identifying polarity of sentiment words only consider the positive and negative polarity by the Cantor set, and no attention is paid to the fuzziness of the polarity intensity of sentiment words. In order to improve the performance, we propose a fuzzy computing model to identify the polarity of Chinese sentiment words in this paper. There are three major contributions in this paper. Firstly, we propose a method to compute polarity intensity of sentiment morphemes and sentiment words. Secondly, we construct a fuzzy sentiment classifier and propose two different methods to compute the parameter of the fuzzy classifier. Thirdly, we conduct extensive experiments on four sentiment words datasets and three review datasets, and the experimental results indicate that our model performs better than the state-of-the-art methods.

Authors

  • Bingkun Wang
    Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
  • Yongfeng Huang
    Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
  • Xian Wu
    Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Xing Li
    Department of Nutrition and food hygiene, College of Public Health of Zhengzhou University, Zhengzhou, China, 450001. Electronic address: 526924683@qq.com.