DocumentCode :
3728218
Title :
Discerning the Trend: Concealing Deceptive Reviews
Author :
Aakas Zhiyuli;Xun Liang;Yige Wang
Author_Institution :
Sch. of Inf., Renmin Univ. of China, Beijing, China
fYear :
2015
Firstpage :
1833
Lastpage :
1838
Abstract :
In this paper, we present Cdear, a novel system to detect fake reviews by using sentiment analysis on attributes of products. We formulate review spam detection as an opinion coincided problem. Specifically, we try to capture the sentiment diverse of attributes of products among different consumers. To our knowledge, this is the very first attempt to use sentiment analysis on attributes of products in reviews to detect review spam. To evaluate the effectiveness of our system, we conduct the experiments on the real life datasets and employ 20 experts to assess the reliability of reviews by carrying out a simulation of shopping online. Meanwhile, we developed an evaluation system to help those experts to assess the reviews, thus to ensure the scientificity of experiments. Comparison between the automatic results of proposed system and human evaluated results demonstrates that the sentiment-based method matches approximately with human perception of reviews´ reliability with 82.66% accuracy.
Keywords :
"Sentiment analysis","Reliability","Algorithm design and analysis","Computational modeling","Buildings","Support vector machines","Time series analysis"
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/SMC.2015.321
Filename :
7379453
Link To Document :
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