识别社会媒体信息中的政治话题:基于词典的方法

Sam Jackson, Feifei Zhang, O. Boichak, Lauren Bryant, Yingya Li, Jeff J. Hemsley, Jennifer Stromer-Galley, Bryan C. Semaan, Nancy J. McCracken
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引用次数: 9

摘要

在本文中,我们介绍了一种基于词典的方法来识别社交媒体消息中的政治话题。在讨论了用于此任务的无监督主题识别的几个关键缺点之后,我们描述了基于词典的方法。我们用2016年美国总统大选中候选人在Facebook和Twitter上发布的竞选信息来测试我们的词汇。结果表明,这种方法为9个政治主题类别中的8个提供了可靠的结果。最后,我们描述了改进我们方法的步骤,以及如何将其用于未来对社交媒体信息中政治主题的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying Political Topics in Social Media Messages: A Lexicon-Based Approach
In this paper, we introduce a lexicon-based method for identifying political topics in social media messages. After discussing several critical shortcomings of unsupervised topic identification for this task, we describe the lexicon-based approach. We test our lexicon on candidate-generated campaign messages on Facebook and Twitter in the 2016 U.S. presidential election. The results show that this approach provides reliable results for eight of nine political topic categories. In closing, we describe steps to improve our approach and how it can be used for future research on political topics in social media messages.
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