Short Text Sentiment Entropy Optimization Based on the Fuzzy Sets

摘要：

　　Short text is the most commonly used form of expression in the network.As short texts like microblog do not provide sufficient word occurrences, sentiment classification methods that use traditional approaches have limitations.In this paper, we propose a short text sentiment classification model called FECEM base on short text entropy optimization method.This method first selects sentiment features based on expectation cross entropy, and then fuzzy sets is used to correct the degree of the comment words.Experiments show that our method is more efficient than the SVM+Maximum Entropy and SVM+chi-square methods, and this new method is robust across different types of short text.

关键词： expectation cross entropy short text sentiment classification fuzzy sets emotional feature

作者: Tao Jiang Bin Yuan Jing Jiang Hongzhi Yu

作者单位: State Key Laboratory of National Languages Information Technology Northwest University for Nationalities Lanzhou,Gansu, P.R.China

会议类型: 国际会议

会议名称: The 12th Web Information System and Application Conference第十二届全国Web信息系统及其应用学术会议(WISA2015)、全国第十次语义Web 与本体论学术研讨会(SWON2015)、全国第九次电子政务技术及应用学术研讨会(EGTA2015)

会议地点: 济南

会议语种:英文

页码: 247-250

在线出版日期: 2015-09-11（万方平台首次上网日期，不代表论文的发表时间）