Tracking sentiment and topic dynamics from social media

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM)
PublisherAssociation for the Advancement of Artificial Intelligence
Pages483-486
Number of pages4
Publication statusPublished - 2012

Cite this

He, Y., Lin, C., Gao, W., & Wong, K-F. (2012). Tracking sentiment and topic dynamics from social media. In Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM) (pp. 483-486). Association for the Advancement of Artificial Intelligence. http://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4496