[Li, Junjie; Zong, Chengqing] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China.
[Yang, Haitong] Univ Chinese Acad Sci, Beijing, Peoples R China.
[Li, Junjie; Zong, Chengqing] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
通讯机构：
[Zong, Chengqing] C
Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China.
Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
语种：
英文
期刊：
Lecture Notes in Computer Science
ISSN：
0302-9743
年：
2016
卷：
10102
页码：
583-594
会议名称：
5th International Conference on Natural Language Processing and Chinese Computing (NLPCC) / 24th International Conference on Computer Processing of Oriental Languages (ICCPOL)
会议论文集名称：
Lecture Notes in Computer Science
会议时间：
DEC 02-06, 2016
会议地点：
Kunming Univ Sci & Technol, Kunming, PEOPLES R CHINA
会议主办单位：
Kunming Univ Sci & Technol
会议赞助商：
China Comp Federat, State Key Lab Digital Publishing, Microsoft Res Asia, DeepShare, Baidu Inc, GridSum, Sogou Inc, RSVP Technologies Inc, NiuTrans, Asian Federat Nat Language Proc, Chinese & Oriental Language Comp Soc
Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61333018]
机构署名：
本校为通讯机构
院系归属：
计算机学院
摘要：
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 poste...
