Predicting User-Topic Opinions in Twitter with Social and Topical Context
Predicting User-Topic Opinions in Twitter with Social and Topical Context
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DOI：
10.1109/t-affc.2013.22
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发表时间：
2013-10
影响因子：
11.2
通讯作者：
F. Ren;Ye Wu
中科院分区：
文献类型：
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作者：
F. Ren;Ye Wu
有了像Twitter这样的流行微博服务，用户能够以更方便的方式在线分享他们的实时感受。用户在推特上生成的数据因此被视为提供个人自发情感信息的资源，引起了研究者的极大关注。之前的工作已经测量了用户推文中的情感表达，然后进行了各种分析和学习。然而，如何利用从观察到的推文中学到的知识和上下文信息来预测用户对他们尚未直接给出的特定主题的看法，这是一个挑战和机遇并存的新问题。在本文中，我们主要致力于用社会语境和话题语境相结合的矩阵分解(ScTcMF)框架来解决这一问题。在真实的Twitter数据集上的实验结果表明，该框架的性能优于现有的协同过滤方法，并证明了社交上下文和话题上下文都能有效地提高用户话题观点预测的性能。
With popular microblogging services like Twitter, users are able to online share their real-time feelings in a more convenient way. The user generated data in Twitter is thus regarded as a resource providing individuals' spontaneous emotional information, and has attracted much attention of researchers. Prior work has measured the emotional expressions in users' tweets and then performed various analysis and learning. However, how to utilize those learned knowledge from the observed tweets and the context information to predict users' opinions toward specific topics they had not directly given yet, is a novel problem presenting both challenges and opportunities. In this paper, we mainly focus on solving this problem with a Social context and Topical context incorporated Matrix Factorization (ScTcMF) framework. The experimental results on a real-world Twitter data set show that this framework outperforms the state-of-the-art collaborative filtering methods, and demonstrate that both social context and topical context are effective in improving the user-topic opinion prediction performance.