Topic discovery from tweet replies

Twitter is a popular online social information network service which allows people to read and post messages up to 140 characters, known as “tweets”. In this paper, we focus on the tweets between pairs of individuals, i.e., the tweet replies, and propose a generative model to discover topics among g...

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Main Authors:	DAI, Bingtian, LIM, Ee Peng, PRASETYO, Philips Kokoh
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2012
Subjects:	Databases and Information Systems Numerical Analysis and Scientific Computing Social Media
Online Access:	https://ink.library.smu.edu.sg/sis_research/3160 https://ink.library.smu.edu.sg/context/sis_research/article/4161/viewcontent/TopicDiscoveryTweetReplies_2012_MLG.pdf
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Institution:	Singapore Management University
Language:	English

id	sg-smu-ink.sis_research-4161
record_format	dspace
spelling	sg-smu-ink.sis_research-41612018-06-19T05:44:48Z Topic discovery from tweet replies DAI, Bingtian LIM, Ee Peng PRASETYO, Philips Kokoh Twitter is a popular online social information network service which allows people to read and post messages up to 140 characters, known as “tweets”. In this paper, we focus on the tweets between pairs of individuals, i.e., the tweet replies, and propose a generative model to discover topics among groups of twitter users. Our model has then been evaluated with a tweet dataset to show its effectiveness. 2012-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3160 https://ink.library.smu.edu.sg/context/sis_research/article/4161/viewcontent/TopicDiscoveryTweetReplies_2012_MLG.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing Social Media
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Databases and Information Systems Numerical Analysis and Scientific Computing Social Media
spellingShingle	Databases and Information Systems Numerical Analysis and Scientific Computing Social Media DAI, Bingtian LIM, Ee Peng PRASETYO, Philips Kokoh Topic discovery from tweet replies
description	Twitter is a popular online social information network service which allows people to read and post messages up to 140 characters, known as “tweets”. In this paper, we focus on the tweets between pairs of individuals, i.e., the tweet replies, and propose a generative model to discover topics among groups of twitter users. Our model has then been evaluated with a tweet dataset to show its effectiveness.
format	text
author	DAI, Bingtian LIM, Ee Peng PRASETYO, Philips Kokoh
author_facet	DAI, Bingtian LIM, Ee Peng PRASETYO, Philips Kokoh
author_sort	DAI, Bingtian
title	Topic discovery from tweet replies
title_short	Topic discovery from tweet replies
title_full	Topic discovery from tweet replies
title_fullStr	Topic discovery from tweet replies
title_full_unstemmed	Topic discovery from tweet replies
title_sort	topic discovery from tweet replies
publisher	Institutional Knowledge at Singapore Management University
publishDate	2012
url	https://ink.library.smu.edu.sg/sis_research/3160 https://ink.library.smu.edu.sg/context/sis_research/article/4161/viewcontent/TopicDiscoveryTweetReplies_2012_MLG.pdf
_version_	1770572894825349120

Topic discovery from tweet replies

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