International Journal of Computational Intelligence Systems

Volume 11, Issue 1, 2018, Pages 634 - 651

On-line Evolutionary Sentiment Topic Analysis Modeling

Authors
YongHeng Chen1, 2, YH_chen@mnnu.edu.cn, ChunYan Yin1, YaoJin Lin2, yjlin@mnnu.edu.cn, Wanli Zuo3, wanli@jlu.edu.cn
1School of Information Engineering, Lingnan Normal University, Zhanjiang, Guangdong, China
2College of Computer Science, Minnan Normal University, Zhangzhou, Fujian 363000, China
Key Laboratory of Data Science and Intelligence Application, Fujian Province University
3College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China
Received 3 June 2017, Accepted 6 January 2018, Available Online 22 January 2018.
DOI
https://doi.org/10.2991/ijcis.11.1.49How to use a DOI?
Keywords
topic minding; sentiment analysis; nonparametric Bayesian statistics; Markov chain Monte Carlo
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.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
11 - 1
Pages
634 - 651
Publication Date
2018/01/22
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.11.1.49How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - YongHeng Chen
AU  - ChunYan Yin
AU  - YaoJin Lin
AU  - Wanli Zuo
PY  - 2018
DA  - 2018/01/22
TI  - On-line Evolutionary Sentiment Topic Analysis Modeling
JO  - International Journal of Computational Intelligence Systems
SP  - 634
EP  - 651
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.49
DO  - https://doi.org/10.2991/ijcis.11.1.49
ID  - Chen2018
ER  -