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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Main Authors: ZHAO, Wayne Xin, JIANG, Jing, WENG, Jianshu, HE, Jing, LIM, Ee Peng, YAN, Hongfei, LI, Xiaoming
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Published: Institutional Knowledge at Singapore Management University 2011
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Online Access:https://ink.library.smu.edu.sg/researchdata/12
https://github.com/minghui/Twitter-LDA
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Institution: Singapore Management University
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spelling 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
institution Singapore Management University
building SMU Libraries
country Singapore
collection InK@SMU
topic Computer Sciences
Databases and Information Systems
spellingShingle Computer Sciences
Databases and Information Systems
ZHAO, Wayne Xin
JIANG, Jing
WENG, Jianshu
HE, Jing
LIM, Ee Peng
YAN, Hongfei
LI, Xiaoming
Twitter-LDA
description 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.
format 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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