Text mining has played a important role in providing product recommendations to users. Online reviews have become an important factor when people make purchase and business decisions. Efficient recommendation systems help in improving business and also enhance customer satisfaction. The credibility of purchasing a product highly depends on the e-commerce online reviews. However most of people wrongly promote or demote a product by buying and selling fake reviews. Many websites have become source of such opinion spam. These fake/fraudulent reviews are deliberately written to trick potential customers in order to promote/hype them or defame their reputations. Our work is aimed at identifying whether a review is fake or truthful one. Naïve Bayes Classifier, Logistic regression and Support Vector Machines are the classifiers using in our work. This in turns leads to recommending undeserving products. This paper aims to classify online reviews into groups of positive or negative polarity by using machine learning algorithms. In this study, we find online 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 Neighbours (KNN-IBK) for sentiment classification of reviews using two different datasets, including movie review dataset and movie reviews dataset. 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.

Amazon E-Commerce dataset, Active Learning, Dataset acquisition, Data pre-processing, KNN Classifier, Rough Set Classifier, Support Vector Machine

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