Personality-based refinement for sentiment classification in microblog

doi:10.1016/j.knosys.2017.06.031

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 互联网大数据与安全信息学研究中心

	Personality-based refinement for sentiment classification in microblog
	Lin, Junjie1 ; Mao, Wenji1,2 ; Zeng, Daniel D.1,2
刊名	KNOWLEDGE-BASED SYSTEMS
	2017-09-15
卷号	132 期号:132 页码:204-214
关键词	Sentiment Classification Social Media Analytics Personality Prediction Big Five Model
DOI	10.1016/j.knosys.2017.06.031
文献子类	Article
英文摘要	Microblog has become one of the most widely used social media for people to share information and express opinions. As information propagates fast in social network, understanding and analyzing public sentiment implied in user-generated content is beneficial for many fields and has been applied to applications such as social management, business and public security. Most previous work on sentiment analysis makes no distinctions of the tweets by different users and ignores the diverse word use of people. As some sentiment expressions are used by specific groups of people, the corresponding textual sentiment features are often neglected in the analysis process. On the other hand, previous psychological findings have shown that personality influences the ways people write and talk, suggesting that people with same personality traits tend to choose similar sentiment expressions. Inspired by this, in this paper we propose a method to facilitate sentiment classification in microblog based on personality traits. To this end, we first develop a rule-based method to predict users' personality traits based on the most well studied personality model, the Big Five model. In order to leverage more effective but not widely used sentiment features, we then extract those features grouped by different personality traits and construct personality-based sentiment classifiers. Moreover, we adopt an ensemble learning strategy to integrate traditional textual feature based and our personality-based sentiment classification. Experimental studies on Chinese microblog dataset show the effectiveness of our method in refining the performance of both the traditional and state-of-the-art sentiment classifiers. Our work is among the first to explicitly explore the role of user's personality in social media analytics and its application in sentiment classification. (C) 2017 Elsevier B.V. All rights reserved.
WOS关键词	TEXT ; WEB
WOS研究方向	Computer Science
语种	英语
WOS记录号	WOS:000407184900017
资助机构	National Natural Science Foundation of China(61671450 ; Ministry of Science and Technology of China(2016YFC1200702) ; 71621002)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/19846]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
作者单位	1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
推荐引用方式 GB/T 7714	Lin, Junjie,Mao, Wenji,Zeng, Daniel D.. Personality-based refinement for sentiment classification in microblog[J]. KNOWLEDGE-BASED SYSTEMS,2017,132(132):204-214.
APA	Lin, Junjie,Mao, Wenji,&Zeng, Daniel D..(2017).Personality-based refinement for sentiment classification in microblog.KNOWLEDGE-BASED SYSTEMS,132(132),204-214.
MLA	Lin, Junjie,et al."Personality-based refinement for sentiment classification in microblog".KNOWLEDGE-BASED SYSTEMS 132.132(2017):204-214.