• 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