Online Sentiment and Topic Dynamics Tracking over the Streaming Data

We propose a dynamic joint sentiment-topic model (dJST) which is able to effectively track sentiment and topic dynamics over the streaming data. 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 study three different ways of accounting for such dependency information, (1) Sliding window, where the current sentiment-topic-word distributions are dependent on the previous sentiment-topic specific word distributions in the last $S$ epochs, (2) Skip model, where history sentiment-topic-word distributions are considered by skipping some epochs in between, and (3) Multiscale model, where previous long- and short-timescale distributions are taken into consideration. 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.