On-line Evolutionary Sentiment Topic Analysis Modeling

Clicks: 295
ID: 51408
2018
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Abstract
As the rapid booming of reviews, a valid sentiment analysis model will significantly boost the review recommendation system’s capability, and present more constructive information for consumers. Topic probabilistic models have already shown many advantages for detecting potential structure of topics and sentiments in reviews corpus. However, most reviews are presented through time-dependent data streams and some respects of the potential structure are unfixed and time-varying, such as topic number and word probability distribution. In this paper, a novel probabilistic topic modelling framework is proposed, called on-line evolutionary sentiment/topic modeling (OESTM), which has the capacity for achieving the optimization of the aforementioned aspects. Firstly, OESTM depends on an improved non-parametric Bayesian model for estimating the best number of topics that can perfectly explain the current time-slice, and analyzes these latent topics and sentiment polarities simultaneously. Secondly, OESTM implements the birth, death and inheritance for detected topics through the transfer of parameters from previous time slices to the updated time slice. The experiments show that significant improvements have been achieved by the proposed model with respect to other state-of-the-art models.
Reference Key
chen2018onlineinternational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chen, YongHeng;Yin, ChunYan;Lin, YaoJin;Zuo, Wanli;
Journal international journal of computational intelligence systems
Year 2018
DOI
DOI not found
URL
https://www.atlantis-press.com/article/25892517/view
Keywords
Education Medicine Information technology Computer applications to medicine. Medical informatics environmental effects of industries and plants renewable energy sources environmental sciences psychology business electronic computers. computer science

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