Tracking sentiment and topic dynamics from social media

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

Research output: Chapter in Book/Report/Conference proceedingChapter

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 publicationSocial Media Content Analysis
Subtitle of host publicationNatural Language Processing and Beyond
PublisherWorld Scientific Publishing Co. Pte Ltd
Pages457-465
Number of pages9
ISBN (Electronic)9789813223615
ISBN (Print)9789813223608
DOIs
Publication statusPublished - 1 Jan 2017

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

He, Y., Lin, C., Gao, W., & Wong, K. F. (2017). Tracking sentiment and topic dynamics from social media. In Social Media Content Analysis: Natural Language Processing and Beyond (pp. 457-465). World Scientific Publishing Co. Pte Ltd. https://doi.org/10.1142/9789813223615_0028