Tracking Sentiment and Topic Dynamics from Social Media


  • Yulan He The Open University
  • Chenghua Lin The Open University
  • Wei Gao Qatar Foundation
  • Kam-Fai Wong The Chinese University of Hong Kong



Sentiment analysis, topic model, dynamic sentiment-topic detection


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.




How to Cite

He, Y., Lin, C., Gao, W., & Wong, K.-F. (2021). Tracking Sentiment and Topic Dynamics from Social Media. Proceedings of the International AAAI Conference on Web and Social Media, 6(1), 483-486.