Tracking sentiment and topic dynamics from social media
Publication Type
Conference Proceeding Article
Publication Date
7-2012
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.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM 2012)
First Page
483
Last Page
486
Publisher
AAAI Press
City or Country
Dublin, Ireland
Citation
HE, Yulan; LIN, Chenghua; GAO, Wei; and WONG, Kam-Fai.
Tracking sentiment and topic dynamics from social media. (2012). Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM 2012). 483-486.
Available at: https://ink.library.smu.edu.sg/sis_research/4611
Additional URL
https://aaai.org/ICWSM/ICWSM12/paper/view/4496