Abstract
In the ongoing era of flourishing e-commerce, people prefer online purchasing products and services to save time. These online purchase decisions are mostly influenced by the reviews/opinions of others who already have experienced them. Malicious users use this experience sharing to promote or degrade products/services for their iniquitous monetary benefits, known as review spam. This study aims to evaluate the performance of ensemble learning on review spam detection with selected features extracted from real and semi-real-life datasets. We study various performance metrics including Precision, Recall, F-Measure, and Receiver Operating Characteristic (RoC). Our proposed ensemble learning module (ELM) with ChiSquared feature selection technique outperformed all others with 0.851 Precision.
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References
A.K. Samha, Y. Li, J. Zhang, Aspect-based opinion extraction from customer reviews, Computer Science and Information Technology (CS and IT), Volume 4, Number 4, in Proceedings of the Second International Conference of Database and Data Mining (DBDM 2014), Dubai, 2014, pp. 149–160.
M. Luca, G. Zervas. Fake it till you make it: reputation, competition, and Yelp review fraud. Manag. Sci. 62(12) (2016), 3412–3427.
N. Jindal, B. Liu, Analyzing and detecting review spam, in Seventh IEEE International Conference on Data Mining (ICDM), Omaha, 2007, pp. 547–552.
N. Jindal, B. Liu, Opinion spam and analysis, in Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM’08), Palo Alto, 2008, pp. 219–230.
D.H. Fusilier, M. Montes-y-Gómez, P. Rosso, R.G. Cabrera. Detecting positive and negative deceptive opinions using PU-learning. Info. Proc. Manag. 51(4) (2015), 433–443.
M. Ott, Y. Choi, C. Cardie, J.T. Hancock, 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-Volume 1, Portland, 2011, pp. 309–319.
M. Ott, C. Cardie, J.T. Hancock, Negative deceptive opinion spam, in The 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2013), Atlanta, 2013, pp. 497–501.
S. Feng, R. Banerjee, Y. Choi, Syntactic stylometry for deception detection, in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, Jeju Island, 2012, pp. 171–175.
J. Li, C. Cardie, S. Li, TopicSpam: a Topic-Model based approach for spam detection, Assoc. Comput. Linguist. 2 (2013), 217–221.
D.H. Fusilier, M. Montes-y-Gómez, P. Rosso, R.G. Cabrera, Detection of opinion spam with character n-grams, in: Computational Linguistics and Intelligent Text Processing, Springer, 2015, pp. 285–294.
F. Khurshid, Y. Zhu, C. W. Yohannese, M. Iqbal, Recital of supervised learning on review spam detection: an empirical analysis, in 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nanjing, 2017, pp. 224–229.
A. Mukherjee, V. Venkataraman, B. Liu, N.S. Glance, What yelp fake review filter might be doing?, in The seventh International AAAI Conference on Weblogs and Social Media (ICWSM), Massachusetts, 2013, pp. 409–418.
M. Crawford, T.M. Khoshgoftaar, J.D. Prusa, A.N. Richter, H. AlNajada. Survey of review spam detection using machine learning tecnoques. J. Big Data. 2 (2015), 1–24.
J. Li, M. Ott, C. Cardie, E.H. Hovy, Towards a general rule for identifying deceptive opinion spam, in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Baltimore, 2014, pp. 1566–1576.
T. Wang, H. Zhu, Voting for deceptive opinion spam detection, arXiv preprint arXiv: 1409.4504, 2014.
S. Banerjee, A. Chua, A linguistic framework to distinguish between genuine and deceptive online reviews, in Proceedings of the International Multi Conference of Engineers and Computer Scientists IMECS, Hong Kong, 2014, Vol I, pp. 501–506.
A. Mukherjee, V. Venkataraman, B. Liu, N. Glance, Fake review detection: classification and analysis of real and pseudo-reviews, Technical Report UIC-CS-2013-03, University of Illinois, Chicago, 2013.
C. Chen, H. Zhao, Y. Yang, Deceptive opinion spam detection using deep level linguistic features, in Natural Language Processing and Chinese Computing - 4th CCF Conference, NLPCC 2015, Springer, Nanchang, Oct. 9–13, 2015, pp. 465–474.
Y. Ren, D. Ji. Neural networks for deceptive opinion spam detection: an empirical study. Info. Sci. 385 (2017), 213–224.
S. Xie, G. Wang, S. Lin, P.S. Yu, Review spam detection via temporal pattern discovery, in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, 2012, pp. 823–831.
N. Jindal, B. Liu, E.-P. Lim, Finding unusual review patterns using unexpected rules, in Proceedings of the 19th ACM International Conference on Information and Knowledge Management, Toronto, 2010, pp. 1549–1552.
H. Li, L. Bing, A. Mukherjee, J. Shao. Spotting fake reviews using positive-unlabeled learning. Comput. Syst. 18(3) (2014), 467–475.
H. Li, Z. Chen, L. Bing, X. Wei, J. Shao, Spotting fake reviews via collective positive-unlabeled learning, in Data Mining (ICDM), 2014 IEEE International Conference on, 2014, pp. 899–904: IEEE.
G. John, P. Langley, Estimating continuous distributions in Bayesian classifiers, in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, 1995, pp. 338–345.
Y. Freund, R.E. Schapire, Experiments with a new boosting algorithm, in Thirteenth International Conference on Machine Learning, San Francisco, 1996, pp. 148–156.
J. Su, H. Zhang, C.X. Ling, S. Matwin, Discriminative parameter learning for Bayesian networks, in The 25th International Conference on Machine Learning (ICML 2008), Helsinki, 2008, pp. 1016–1023.
F. Ahmed, M. Abulaish, A generic statistical approach for spam detection in online social networks. Comput. Commun. 36(10) (2013), 1120–1129.
C.-C. Chang, C.-J. Lin, LIBSVM: a library for support vector machines, ACM Trans. Intell. Syst. Technol. 2(3) (2011), pp. 1–27.
N. Sugiura, Further analysis of the data by Akaike’s information criterion and the finite corrections, Commun. Stat. Theory Methods. 7(1) (1978), 13–26.
A. Moraglio, C. DiChio, R. Poli, Geometric Particle Swarm Optimisation, Springer Berlin Heidelberg, Berlin, Heidelberg, 2007, pp. 125–136.
X.S. Yang, D. Suash, Cuckoo search via Lévy flights, in 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), Coimbatore, 2009, pp. 210–214.
P.E. Black, Greedy algorithm, in: V. Pieterse, P.E. Black (Eds.), Dictionary of Algorithms and Data Structures, Feb. 2, 2005.
K. Pearson, X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling AU, Lond. Edinb. Dublin Philos. Mag. J. Sci. 50 (July 1, 1900), 157–175.
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Khurshid, F., Zhu, Y., Xu, Z. et al. Enactment of Ensemble Learning for Review Spam Detection on Selected Features. Int J Comput Intell Syst 12, 387–394 (2018). https://doi.org/10.2991/ijcis.2019.125905655
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DOI: https://doi.org/10.2991/ijcis.2019.125905655