Dynamic joint sentiment-topic model


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.

Publication DOI: https://doi.org/10.1145/2542182.2542188
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Additional Information: © Copyright: 2013 ACM. Funding: EPSRC [EP/J020427/1]; EC [257859]; Royal Academy of Engineering, UK.
Uncontrolled Keywords: dynamic joint sentiment-topic model,opinion mining,sentiment analysis,topic model,Theoretical Computer Science,Artificial Intelligence
Publication ISSN: 2157-6912
Last Modified: 28 Jun 2024 08:30
Date Deposited: 08 Sep 2014 11:30
Full Text Link: http://dl.acm.o ... .cfm?id=2542188
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2013-12
Authors: He, Yulan (ORCID Profile 0000-0003-3948-5845)
Lin, Chenghua
Gao, Wei
Wong, Kam-Fai



Version: Published Version

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