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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (9): 74-82    DOI: 10.11925/infotech.2096-3467.2017.09.08
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Analyzing Online Reviews with Dynamic Sentiment Topic Model
Hui Li,Yunfeng Hu()
School of Economics and Management, Xidian University, Xi’an 710071, China
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[Objective] This paper analyzes online reviews to identify the patterns of their topic contents and sentiments. [Methods] First, we obtained the sentiment of the reviews with the SSTM model. Then, we proposed a DSTM model based on the document, document sentiment distribution and words. Finally, we estimated the distribution of sentiment-topic and the keywords. [Results] We modeled the review datasets by time slice and found the changing trends of contents and sentiments over time. [Limitations] The proposed model did not include the relationship among different subjects, which might generate errors. [Conclusions] The DSTM model, which integrates the external time features, can effectively analyze the evolution of online review topics.

Key wordsShort-text Sentiment-Topic Model      Dynamic Sentiment Topic Model      Parameter Estimation      Sentiment Online Reviews     
Received: 07 April 2017      Published: 18 October 2017

Cite this article:

Hui Li,Yunfeng Hu. Analyzing Online Reviews with Dynamic Sentiment Topic Model. Data Analysis and Knowledge Discovery, 2017, 1(9): 74-82.

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