Improved LDA Model for Credibility Evaluation of Online Product Reviews

Xuan WANG  Bofeng ZHANG  Mingqing HUANG  Furong CHANG  Zhuocheng ZHOU  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.11   pp.2148-2158
Publication Date: 2019/11/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7243
Type of Manuscript: PAPER
Category: Data Engineering, Web Information Systems
Keyword: 
comment credibility,  biterm sentiment latent Dirichlet allocation model,  unsupervised method,  unequal short text,  

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Summary: 
When individuals make a purchase from online sources, they may lack first-hand knowledge of the product. In such cases, they will judge the quality of the item by the reviews other consumers have posted. Therefore, it is significant to determine whether comments about a product are credible. Most often, conventional research on comment credibility has employed supervised machine learning methods, which have the disadvantage of needing large quantities of training data. This paper proposes an unsupervised method for judging comment credibility based on the Biterm Sentiment Latent Dirichlet Allocation (BS-LDA) model. Using this approach, first we derived some distributions and calculated each comment's credibility score via them. A comment's credibility was judged based on whether it achieved a threshold score. Our experimental results using comments from Amazon.com demonstrated that the overall performance of our approach can play an important role in determining the credibility of comments in some situation.