Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81371
Title: Textual analysis for online reviews : a polymerization topic sentiment model
Authors: Huang, LJ 
Dou, ZX
Hu, YJ
Huang, RY
Keywords: Textual analysis
Sentiment model
Polymerization computing
Online reviews
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE access, 2019, v. 7, p. 91940-91945 How to cite?
Journal: IEEE access 
Abstract: More and more e-commerce companies realize the importance of analyzing the online reviews of their products. It is believed that online review has a significant impact on the shaping product brand and sales promotion. In this paper, we proposed a polymerization topic sentiment model (PTSM) to conduct textual analysis for online reviews. We applied this model to extract and filter the sentiment information from online reviews. Through integrating this model with machine learning methods, the results showed that the prediction accuracy had improved. Also, the experimental results showed that filtering sentiment topics hidden in the reviews are more important in influencing sales prediction, and the PTSM is more precise than other methods. The findings of this paper contribute to the knowledge that filtering the sentiment topics of online reviews could improve the prediction accuracy. Also, it could be applied by e-commerce practitioners as a new technique to conduct analyses of online reviews.
URI: http://hdl.handle.net/10397/81371
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2920091
Rights: © 2019 IEEE. Translations and content mining are permitted for academic research only.
Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
The following publication L. Huang, Z. Dou, Y. Hu and R. Huang, "Textual Analysis for Online Reviews: A Polymerization Topic Sentiment Model," in IEEE Access, vol. 7, pp. 91940-91945, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2920091
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