On-line Evolutionary Sentiment Topic Analysis Modeling
- https://doi.org/10.2991/ijcis.11.1.49How to use a DOI?
- topic minding; sentiment analysis; nonparametric Bayesian statistics; Markov chain Monte Carlo
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.
- © 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 -