@Article{10798587.2016.1267244,
AUTHOR = {Jie Liu, Xiaodong Fu, Jin Liu, Yunchuan Sun},
TITLE = {Analyzing and Assessing Reviews on Jd.com},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {1},
PAGES = {73--80},
URL = {http://www.techscience.com/iasc/v24n1/39727},
ISSN = {2326-005X},
ABSTRACT = {Reviews are contents written by users to express opinions on products or services. The information 
contained in reviews is valuable to users who are going to make decisions on products or services. 
However, there are numbers of reviews for popular products, and the quality of reviews is not always 
good. It’s necessary to pick out reviews, which are in high quality from numbers of reviews to assist user 
in making decision. In this paper, we collected 21,501 reviews flagged as good from 499,253 products 
on JD.com. We observed the level of users is an important factor affects the quality of reviews, and 
users prefer to post short reviews containing the description of the quality and price of the product. 
We proposed a system to assess the quality of reviews automatically in this paper. We achieved that by 
applying SVM classification based on two kinds of features; reviews and reviewers that would help users 
find out high quality reviews and useful information from massive reviews. We evaluated our system on 
JD.com. The accuracy of our experiments for reviews quality assessing reached to 87.5 percent.},
DOI = {10.1080/10798587.2016.1267244}
}