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Detecting Fake Reviews of Hype About Restaurants by Sentiment Analysis

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Web-Age Information Management (WAIM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8597))

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Abstract

Fake reviews in e-commerce website can greatly affect the choice of consumers. By categorizing the set of fake reviews, we find that fake reviews of hype make up the largest part, and this type of review is most misleading as well. We analyzed all the characteristics of fake reviews of hype and find the most evident one is in the text of the review. Usually, hype is completely positive or negative. So this article purposes an algorithm to detect fake reviews of hype about restaurants based on sentiment analysis. Reviews are considered in four dimensions: taste, environment, service and overall attitude, if the analysis result of the four dimensions are consistent, the review will be categorized as hype. The testing result shows that the accuracy of the algorithm is about 74 %. The method can also be applied to other areas, such as the sentiment analysis in emergency management.

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References

  1. Drucker, H., Wu, D., Vapnik, V.N.: Support vector machines for spam categorization. IEEE Trans. Neural Netw. 10(5), 1048–1054 (2002)

    Article  Google Scholar 

  2. Ntoulas, A., Najork, M., Manasse, M., et al.: Detecting spam web pages through content analysis. In: Proceedings of the 15th International World Wide Web Conference (WWW’06), Edinburgh, Scotland, pp. 83–92. ACM, New York, 23–26 May 2006

    Google Scholar 

  3. Jindal, N., Liu, B., Lim, E.: Finding unusual review patterns using unexpected rules. In: The 19th ACM International Conference on Information and Knowledge Management (CIKM-2010), Toronto, Canada, pp. 25–628, 26–30 Oct 2010

    Google Scholar 

  4. Jindal, N, Liu, B.: Opinion spam and analysis. In: Proceedings of the 1st ACM International Conference on Web Search and Data Mining (WSDM’08), California, USA, pp. 137–142. ACM, New York, 11–12 Feb 2008

    Google Scholar 

  5. Wang, G., Xie, S., Liu, B., Yu, P.S.: Review graph based online store review spammer detection. In: 2011 IEEE 11th International Conference on Data Mining (ICDM), Vancouver, BC, pp. 1242–1247, 11–14 Dec 2011

    Google Scholar 

  6. Xie, S., Wang, G., Lin, S., Yu, P.S.: Review spam detection via temporal pattern discovery. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, pp. 823–831, 12–16 Aug 2012

    Google Scholar 

  7. Mukherjee, A., Liu, B., Wang, J., Glance, N., Jindal, N.: Detecting group review spam. In: Proceedings of the 20th International Conference Companion on World Wide Web (WWW ‘11), New York, NY, USA, pp. 93–94 (2011)

    Google Scholar 

  8. Haixia, S., Xin, Y., Zhengtao, Y., et al.: Detection of fake reviews based on adaptive clustering. J. Nanjing Univ. Nat. Sci. 49(4), 38–43 (2013)

    Google Scholar 

  9. Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL HLT), Portland, OR, United States, pp. 309–319, 19–24 June 2011

    Google Scholar 

  10. Wang, J., Wang, L., Gao, W., Yu, J.: A research on the keywords extraction of naive Bayes. Comput. Appl. Softw. 31(2), 174–181 (2014)

    Google Scholar 

  11. Zhang, F., Wu, Z., Yao, F.: Research of spam filter based on Bayes. J. Yanshan Univ. 33(1), 47–52 (2009)

    Google Scholar 

  12. Xia, T., Fan, X., Liu, L.: Implementation of ICTCLAS system based on JNI. Comput. Appl. 24(z2), 945–950 (2004). Deng, J.: Control problems of grey system. Syst. Control Lett., 1, 288–294 (1982)

    Google Scholar 

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Acknowledgement

Thanks to the support of National Natural Science Foundation of China (NNSF) (Grants No.90924029), National Culture Support Foundation Project of China (2013BAH43F01), and National 973 Program Foundation Project of China (2013CB329600).

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Correspondence to Run Yu Chen .

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Chen, R.Y., Guo, J.Y., Deng, X.L. (2014). Detecting Fake Reviews of Hype About Restaurants by Sentiment Analysis. In: Chen, Y., et al. Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science(), vol 8597. Springer, Cham. https://doi.org/10.1007/978-3-319-11538-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-11538-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11537-5

  • Online ISBN: 978-3-319-11538-2

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