Personality-based Refinement for Sentiment Classification in Microblog
Personality-based Refinement for Sentiment Classification in Microblog
复制标题
基于个性的微博情感分类细化
DOI：
10.1016/j.knosys.2017.06.031
复制
发表时间：
2017-09
影响因子：
8.8
通讯作者：
Zeng Daniel D.
中科院分区：
文献类型：
--
作者：
Lin Junjie;Mao Wenji;Zeng Daniel D.
微博已经成为人们分享信息和发表意见使用最广泛的社交媒体之一。随着信息在社交网络中的快速传播，理解和分析用户生成的内容中蕴含的舆情，对许多领域都有好处，并已应用于社会管理、商业和公安等领域。大多数以前的情感分析工作没有区分不同用户的推文，忽略了人们用词的多样性。由于某些情感表达是由特定人群使用的，相应的文本情感特征在分析过程中往往被忽略。另一方面，之前的心理学研究表明，性格会影响人们的写作和说话方式，这表明具有相同性格特征的人倾向于选择相似的情感表达方式。受此启发，本文提出了一种基于个性特征的微博情感分类方法。为此，我们首先在研究最多的人格模型--大五模型的基础上，提出了一种基于规则的方法来预测用户的人格特征。为了利用更有效但不被广泛使用的情感特征，我们提取了按不同个性特征分组的特征，并构建了基于个性的情感分类器。此外，我们还采用了集成学习策略，将传统的基于文本特征的情感分类与基于个性的情感分类相结合。在中文微博数据集上的实验研究表明，该方法能够有效地提高传统情感分类器和最新情感分类器的性能。我们的工作是首次明确探索用户个性在社交媒体分析中的作用及其在情感分类中的应用。
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.
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DOI：
10.1145/2659522.2659529
发表时间：
2014-11
期刊：
--
影响因子：
--
作者：
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通讯作者：
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DOI：
10.3115/v1/s14-2111
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影响因子：
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DOI：
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发表时间：
2007-01
期刊：
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影响因子：
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Eric Breck;Yejin Choi;Claire Cardie
DOI：
--
发表时间：
2007
期刊：
--
影响因子：
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作者：
J. Pennebaker;R. Booth;Martha E. Francis
通讯作者：
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DOI：
10.1145/2380718.2380722
发表时间：
2012-06
期刊：
--
影响因子：
--
作者：
Yoram Bachrach;Michal Kosinskihttps://www.semanticscholar.org/me/account-Michal-Kosinskihttps:-www.semanticscholar.org-me-2654865;T. Graepel;Pushmeet Kohli;D. Stillwell
通讯作者：
Yoram Bachrach;Michal Kosinskihttps://www.semanticscholar.org/me/account-Michal-Kosinskihttps:-www.semanticscholar.org-me-2654865;T. Graepel;Pushmeet Kohli;D. Stillwell