Home // International Journal On Advances in Systems and Measurements, volume 11, numbers 1 and 2, 2018 // View article
Authors:
Elshrif Elmurngi
Abdelouahed Gherbi
Keywords: Sentiment Analysis; Fake Reviews; Naïve Bayes; Support Vector Machine; k-Nearest Neighbor; KStar; Decision Tree -J48.
Abstract:
In recent years, 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 three different datasets, including movie review dataset V1.0 and movie reviews dataset V2.0 and movie reviews dataset V3.0. To evaluate the performance of sentiment classification, this work has implemented accuracy, precision, recall and F-measure as a performance measure. The measured results of our experiments show that the SVM algorithm outperforms other algorithms, and that it reaches the highest accuracy not only in text classification, but also in detecting fake reviews.
Pages: 196 to 207
Copyright: Copyright (c) to authors, 2018. Used with permission.
Publication date: June 30, 2018
Published in: journal
ISSN: 1942-261x