Sentiment Classification of Social Media Text Considering User Attributes

摘要：

　　Social media texts pose a great challenge to sentiment classi fication.Existing classification methods focus on exploiting sophisticated features or incorporating user interactions,such as following and retweeting.Nevertheless,these methods ignore user attributes such as age,gender and location,which is proved to be a very important prior in determining sentiment polarity according to our analysis.In this paper,we propose two algorithms to make full use of user attributes: 1)incorporate them as simple features,2)design a graph-based method to model relationship between tweets posted by users with similar attributes.The extensive experiments on seven movie datasets in Sina Weibo show the superior performance of our methods in handling these short and informal texts.

作者: Junjie Li Haitong Yang Chengqing Zong

作者单位: National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences;Schoo University of Chinese Academy of Sciences,Beijing,China

会议类型: 国际会议

会议名称: 第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)

会议地点: 昆明

会议语种:英文

页码: 1-12

在线出版日期: 2016-12-02（万方平台首次上网日期，不代表论文的发表时间）