Chinese Microblog Sentiment Analysis Based on Sentiment Features

摘要：

　　As the microblog has increasingly become an information platform for netizens to share their ideas,the study on the sentiment analysis of microblog has got scholarswide attention both at home and abroad.The primary goal of this research is to improve the accuracy of microblog sentiment polarity classification.With a view to the characteristics of microblog,a new method of semantically related feature extraction is proposed.Firstly,the Chinese word features are selected by text presentation in VSM and computing the weight by TF*IDF.Secondly,the proposed eight microblog semantic features are extracted,including sentence sentiment judgment based on emotional dictionary.Finally,three kinds of machine learning methods are used to classify the Chinese microblog under the feature vector combining the two methods.The experimental results indicate that the proposed feature extraction method outperforms the state-of-the-art approaches,and for this feature extraction algorithm,the classification performance is best when using the Na(i)ve Bayes algorithm.

关键词： Semantic feature Sentiment classification Machine learning Microblog

作者: Weiwei Li Yuqiang Li Yan Wang

作者单位: School of Computer Science and Technology,Wuhan University of Technology,Wuhan 430063,China

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 385-388

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）