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Improving Opinion Spam Detection by Cumulative Relative Frequency Distribution

2020-12-27 10:23:44
Michela Fazzolari, Francesco Buccafurri, Gianluca Lax, Marinella Petrocchi
arXiv_CL
       
Abstract

Over the last years, online reviews became very important since they can influence the purchase decision of consumers and the reputation of businesses, therefore, the practice of writing fake reviews can have severe consequences on customers and service providers. Various approaches have been proposed for detecting opinion spam in online reviews, especially based on supervised classifiers. In this contribution, we start from a set of effective features used for classifying opinion spam and we re-engineered them, by considering the Cumulative Relative Frequency Distribution of each feature. By an experimental evaluation carried out on real data from this http URL, we show that the use of the distributional features is able to improve the performances of classifiers.

Abstract (translated)

URL

https://arxiv.org/abs/2012.13905

PDF

https://arxiv.org/pdf/2012.13905.pdf

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