10.0166/FK2.STAGEFIGSHARE.6770252.V1
Xin ZHAO Wayne
Jing JIANG
Jianshu WENG
Jing HE
Ee Peng LIM
Hongfei YAN
Xiaoming LI
Twitter-LDA
<p>Latent Dirichlet Allocation (LDA) has been widely used in textual analysis. The original LDA is used to find hidden "topics" in the documents, where a topic is a subject like "arts" or "education" that is discussed in the documents. The original setting in LDA, where each word has a topic label, may not work well with Twitter as tweets are short and a single tweet is more likely to talk about one topic. Hence, Twitter-LDA (T-LDA) has been proposed to address this issue. T-LDA also addresses the noisy nature of tweets, where it captures background words in tweets. As experiments in [7] have shown that T-LDA could capture more meaningful topics than LDA in Microblogs.</p>
<p>The original setting in Latent Dirichlet Allocation (LDA), where each word has a topic label, may not work well with Twitter as tweets are short and a single tweet is more likely to talk about one topic. Hence, Twitter-LDA (T-LDA) has been proposed to address this issue. T-LDA also addresses the noisy nature of tweets, where it captures background words in tweets.</p><p>Related Publication: Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E. P., Yan, H., & Li, X. (2011). Comparing twitter and traditional media using topic models. In <em>Advances in Information Retrieval</em> (pp. 338-349). http://dx.doi.org/10.1007/978-3-642-20161-5_34</p>
Uncategorised content
Singapore Management University
2011
2020-03-09
2020-03-13
Dataset
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10.0166/FK2.stagefigshare.6770252
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