Twitter-LDA
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 ea...
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Institutional Knowledge at Singapore Management University
2011
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在線閱讀: | https://ink.library.smu.edu.sg/researchdata/12 https://github.com/minghui/Twitter-LDA |
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總結: | 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.
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. |
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