Abstract
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.
Original language | English |
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Title of host publication | Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM) |
Publisher | Association for the Advancement of Artificial Intelligence |
Pages | 483-486 |
Number of pages | 4 |
Publication status | Published - 2012 |
Bibliographical note
AcknowledgementsThe authors would like to thank Dong Liu for crawling Mozilla add-ons review data. This work was partially supported by the EC-FP7 project ROBUST (grant number 257859) and the Short Award funded by the Royal Academy of Engineering, UK.