title: A Fuzzy Computing Model for Identifying Polarity of Chinese Sentiment Words.
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.affiliation: Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
authors.forename: Bingkun
authors.identifier: 0000-0002-1878-5454
authors.initials: B
authors.lastname: Wang
authors.affiliation: Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
authors.forename: Yongfeng
authors.initials: Y
authors.lastname: Huang
authors.affiliation: Beijing University of Posts and Telecommunications, Beijing 100876, China.
authors.forename: Xian
authors.initials: X
authors.lastname: Wu
authors.affiliation: Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
authors.forename: Xing
authors.initials: X
authors.lastname: Li
mesh_terms: D003198:Computer Simulation; D017143:Fuzzy Logic; D006801:Humans; D009323:Natural Language Processing; D009862:Online Systems; D012660:Semantics; D014825:Vocabulary