Tracking sentiment and topic dynamics from social media

We propose a dynamic joint sentiment-topic model (dJST) which allows the detection and tracking of views of current and recurrent interests and shifts in topic and sentiment. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic specific word distributions are g...

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Main Authors:	HE, Yulan, LIN, Chenghua, GAO, Wei, WONG, Kam-Fai
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2012
Subjects:	Databases and Information Systems
Online Access:	https://ink.library.smu.edu.sg/sis_research/4611
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Institution:	Singapore Management University
Language:	English

id	sg-smu-ink.sis_research-5614
record_format	dspace
spelling	sg-smu-ink.sis_research-56142019-12-26T06:12:03Z Tracking sentiment and topic dynamics from social media HE, Yulan LIN, Chenghua GAO, Wei WONG, Kam-Fai We propose a dynamic joint sentiment-topic model (dJST) which allows the detection and tracking of views of current and recurrent interests and shifts in topic and sentiment. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic specific word distributions are generated according to the word distributions at previous epochs. We derive efficient online inference procedures to sequentially update the model with newly arrived data and show the effectiveness of our proposed model on the Mozilla add-on reviews crawled between 2007 and 2011. 2012-07-07T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/4611 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
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
spellingShingle	Databases and Information Systems HE, Yulan LIN, Chenghua GAO, Wei WONG, Kam-Fai Tracking sentiment and topic dynamics from social media
description	We propose a dynamic joint sentiment-topic model (dJST) which allows the detection and tracking of views of current and recurrent interests and shifts in topic and sentiment. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic specific word distributions are generated according to the word distributions at previous epochs. We derive efficient online inference procedures to sequentially update the model with newly arrived data and show the effectiveness of our proposed model on the Mozilla add-on reviews crawled between 2007 and 2011.
format	text
author	HE, Yulan LIN, Chenghua GAO, Wei WONG, Kam-Fai
author_facet	HE, Yulan LIN, Chenghua GAO, Wei WONG, Kam-Fai
author_sort	HE, Yulan
title	Tracking sentiment and topic dynamics from social media
title_short	Tracking sentiment and topic dynamics from social media
title_full	Tracking sentiment and topic dynamics from social media
title_fullStr	Tracking sentiment and topic dynamics from social media
title_full_unstemmed	Tracking sentiment and topic dynamics from social media
title_sort	tracking sentiment and topic dynamics from social media
publisher	Institutional Knowledge at Singapore Management University
publishDate	2012
url	https://ink.library.smu.edu.sg/sis_research/4611
_version_	1770574929899552768

Tracking sentiment and topic dynamics from social media

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