Dynamic joint sentiment-topic model

Yulan He*, Chenghua Lin, Wei Gao, Kam-Fai Wong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number6
Number of pages21
JournalACM Transactions on Intelligent Systems and Technology
Issue number1
Publication statusPublished - Dec 2013

Bibliographical note

© Copyright: 2013 ACM.

Funding: EPSRC [EP/J020427/1]; EC [257859]; Royal Academy of Engineering, UK.


  • dynamic joint sentiment-topic model
  • opinion mining
  • sentiment analysis
  • topic model


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