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Detecting Fake Reviews through Sentiment Analysis Using Machine Learning Techniques

Authors:
Elshrif Elmurngi
Abdelouahed Gherbi

Keywords: Sentiment Analysis; Fake Reviews; Naïve Bayes; Support Vector Machine; k-Nearest Neighbor; KStar; Decision Tree -J48.

Abstract:
Recently, Sentiment Analysis (SA) has become one of the most interesting topics in text analysis due to its promising commercial benefits. One of the main issues facing SA is how to extract emotions inside the opinion, and how to detect fake positive reviews and fake negative reviews from opinion reviews. Moreover, the opinion reviews obtained from users can be classified into positive or negative reviews, which can be used by a consumer to select a product. This paper aims to classify movie reviews into groups of positive or negative polarity by using machine learning algorithms. In this study, we analyse online movie reviews using SA methods in order to detect fake reviews. SA and text classification methods are applied to a dataset of movie reviews. More specifically, we compare five supervised machine learning algorithms: Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN-IBK), KStar (K*) and Decision Tree (DT-J48) for sentiment classification of reviews using two different datasets, including movie review dataset V2.0 and movie review dataset V1.0. The measured results of our experiments show that the SVM algorithm outperforms other algorithms, and it reaches the highest accuracy not only in text classification, but also in detecting fake reviews.

Pages: 65 to 72

Copyright: Copyright (c) IARIA, 2017

Publication date: November 12, 2017

Published in: conference

ISSN: 2308-4464

ISBN: 978-1-61208-603-3

Location: Barcelona, Spain

Dates: from November 12, 2017 to November 16, 2017