CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Sentiment Classification of Social Media Text Considering User Attribute
Li, Junjie1,2; Yang, Haitong3; Zong,Chengqing1,2
Conference NameThe Fifth Conference on Natural Language Processing and Chinese Computing & The Twenty Fourth International Conference on Computer Processing of Oriental Languages
Conference Date2016-12
Conference PlaceKunming, China

Social media texts pose a great challenge to sentiment classification. 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.

Indexed ByEI
Document Type会议论文
Corresponding AuthorZong,Chengqing
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China
2.University of Chinese Academy of Sciences, Beijing, China
3.School of Computer, Central China Normal University, Wuhan 430079, China
Recommended Citation
GB/T 7714
Li, Junjie,Yang, Haitong,Zong,Chengqing. Sentiment Classification of Social Media Text Considering User Attribute[C],2016.
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