Meaning as collective use: predicting semantic hashtag categories on twitter
Meaning as collective use: predicting semantic hashtag categories on twitter
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集体使用的意义：预测 Twitter 上的语义主题标签类别
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
M. Strohmaier
中科院分区：
文献类型：
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作者：
Lisa Posch;Claudia Wagner;Philipp Singer;M. Strohmaier
本文旨在探讨Twitter上的标签使用数据是否包含有关其语义的信息。为此，我们进行了初步的统计假设检验，以量化使用模式和标签语义之间的关联。为了评估实用功能的效用-它描述了如何随着时间的推移使用标签-标签的语义分析，我们进行各种标签流分类实验，并比较其效用与词汇功能的效用。我们的研究结果表明，语用功能确实包含有价值的信息分类到语义类别的标签。虽然在我们的实验中，语用特征并没有优于词汇特征，但我们认为，语用特征对于文本信息可能稀疏或缺失的环境是重要的和相关的（例如，在社交视频流中）。
This paper sets out to explore whether data about the usage of hashtags on Twitter contains information about their semantics. Towards that end, we perform initial statistical hypothesis tests to quantify the association between usage patterns and semantics of hashtags. To assess the utility of pragmatic features - which describe how a hashtag is used over time - for semantic analysis of hashtags, we conduct various hashtag stream classification experiments and compare their utility with the utility of lexical features. Our results indicate that pragmatic features indeed contain valuable information for classifying hashtags into semantic categories. Although pragmatic features do not outperform lexical features in our experiments, we argue that pragmatic features are important and relevant for settings in which textual information might be sparse or absent (e.g., in social video streams).