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Tracking Dynamics of Opinion Behaviors with a Content-based Sequential Opinion Influence Model
IEEE Transactions on Affective Computing ( IF 11.2 ) Pub Date : 2020-10-01 , DOI: 10.1109/taffc.2018.2821123
Chengyao Chen , Zhitao Wang , Wenjie Li

Nowadays, social media has become a popular channel for people to exchange opinions through the user-generated text. Exploring the mechanisms about how customers’ opinions towards products are influenced by friends, and further predicting their future opinions have attracted great attention from corporate administrators and researchers. Various influence models have already been proposed for the opinion prediction problem. However, they largely formulate opinions as derived sentiment categories or values but ignore the role of the content information. Besides, existing models only make use of the most recently received information without taking into consideration the long-term historical communication. To keep track of user opinion behaviors and infer user opinion influence from the historical exchanged textual information, we develop a content-based sequential opinion influence framework. Based on this framework, two opinion sentiment prediction models with alternative prediction strategies are proposed. In the experiments conducted on three Twitter datasets, the proposed models outperform other popular influence models. An interesting finding based on a further analysis of user characteristic is that an individuals influence is correlated to her/his style of expressions.

中文翻译:

使用基于内容的顺序意见影响模型跟踪意见行为的动态

如今,社交媒体已成为人们通过用户生成的文本交换意见的流行渠道。探索客户对产品的意见如何受朋友影响的机制,并进一步预测他们未来的意见,引起了企业管理人员和研究人员的极大关注。已经为意见预测问题提出了各种影响模型。然而,他们在很大程度上将观点表述为派生的情感类别或价值观,而忽略了内容信息的作用。此外,现有模型仅使用最近收到的信息,而没有考虑长期的历史交流。跟踪用户意见行为并从历史交换的文本信息中推断用户意见影响,我们开发了一个基于内容的顺序意见影响框架。基于该框架,提出了两种具有替代预测策略的观点情感预测模型。在对三个 Twitter 数据集进行的实验中,所提出的模型优于其他流行的影响模型。基于对用户特征的进一步分析,一个有趣的发现是个人影响与她/他的表达风格相关。
更新日期:2020-10-01
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