Tracking Sentiment and Topic Dynamics from Social Media

He, Yulan; Lin, Chenghua; Gao, Wei and Wong, Kam-Fai (2017). Tracking Sentiment and Topic Dynamics from Social Media. In: Wong, Kam-Fai; Gao, Wei; Xu, Ruifeng and Li, Wenjie eds. Social Media Content Analysis Natural Language Processing and Beyond. Series on Language Processing, Pattern Recognition, and Intelligent Systems:, 5. World Scientific, pp. 457–465.

DOI: https://doi.org/10.1142/9789813223615_0028

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.

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