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ACSC
2015
IEEE

Dynamic Topic Detection Model by Fusing Sentiment Polarity

8 years 6 months ago
Dynamic Topic Detection Model by Fusing Sentiment Polarity
Traditional static topic models mainly focus on the statistical correlation between words, but ignore the sentiment tendency and the temporal properties which may have great effects on topic detection results. This paper proposed an LDA-based dynamic sentiment-topic (DST) model, which could not only detect and track topics but could also analyse the shift of general’s sentiment tendency towards certain topic. This model combines the data with the sentiment and dynamic properties of time by maximum likelihood estimation and the sliding window. We use Gibbs sampling method to estimate and update model parameters, and use random EM algorithm for model reasoning. Experiments on real dataset demonstrate that DST model outperforms the existing algorithms. .
Xi Ding, Lan-Shan Zhang, Ye Tian, Xiangyang Gong,
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACSC
Authors Xi Ding, Lan-Shan Zhang, Ye Tian, Xiangyang Gong, Wendong Wang
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