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Dynamic joint sentiment-topic model

He, Yulan; Lin, Chenghua; Gao, Wei and Wong, Kam-Fai (2013). Dynamic joint sentiment-topic model. ACM Transactions on Intelligent Systems and Technology, 5(1), article no. 6.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1145/2542182.2542188
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Abstract

Social media data are produced continuously by a large and uncontrolled number of users. The dynamic nature of such data requires the sentiment and topic analysis model to be also dynamically updated, capturing the most recent language use of sentiments and topics in text. 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 study three different ways of accounting for such dependency information, (1) Sliding window where the current sentiment-topic-word distributions are dependent on the previous sentiment-topic specific word distributions in the last S epochs; (2) Skip model where history sentiment-topic-word distributions are considered by skipping some epochs in between; and (3) Multiscale model where previous long- and shorttimescale distributions are taken into consideration. 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.

Item Type: Journal Item
Copyright Holders: 2013 Association for Computing Machinery (ACM)
ISSN: 2157-6912
Extra Information: Special Issue: Social Web Mining, 2012
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 36255
Depositing User: Kay Dave
Date Deposited: 23 Jan 2013 14:16
Last Modified: 02 May 2018 13:49
URI: http://oro.open.ac.uk/id/eprint/36255
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