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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sg-smu-ink.researchdata-10112015-11-25T05:53:01Z Twitter-LDA ZHAO, Wayne Xin JIANG, Jing WENG, Jianshu HE, Jing LIM, Ee Peng YAN, Hongfei LI, Xiaoming 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. 2011-04-01T07:00:00Z text https://ink.library.smu.edu.sg/researchdata/12 https://github.com/minghui/Twitter-LDA SMU Research Data Institutional Knowledge at Singapore Management University Computer Sciences Databases and Information Systems |
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Computer Sciences Databases and Information Systems ZHAO, Wayne Xin JIANG, Jing WENG, Jianshu HE, Jing LIM, Ee Peng YAN, Hongfei LI, Xiaoming Twitter-LDA |
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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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text |
author |
ZHAO, Wayne Xin JIANG, Jing WENG, Jianshu HE, Jing LIM, Ee Peng YAN, Hongfei LI, Xiaoming |
author_facet |
ZHAO, Wayne Xin JIANG, Jing WENG, Jianshu HE, Jing LIM, Ee Peng YAN, Hongfei LI, Xiaoming |
author_sort |
ZHAO, Wayne Xin |
title |
Twitter-LDA |
title_short |
Twitter-LDA |
title_full |
Twitter-LDA |
title_fullStr |
Twitter-LDA |
title_full_unstemmed |
Twitter-LDA |
title_sort |
twitter-lda |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2011 |
url |
https://ink.library.smu.edu.sg/researchdata/12 https://github.com/minghui/Twitter-LDA |
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