| dc.description.abstract | With the advances in information and communication technologies, the immense quantity of review texts have become available on the Web. Review text can serve as an essential source of information for individual decision makers and business organizations. Some of the reviews shared on the Web may contain deceptive information to mislead the existing decision making process. In this study, we have presented a supervised learning based scheme for review spam detection. In the presented study, psychological and linguistic feature sets and their combinations are taken into consideration. In the study, the predictive performances of four conventional supervised learning methods (namely, Naive Bayes classifier, K-nearest neighbor algorithm, support vector machines and C4.5 algorithm) are evaluated on the different feature sets. | |
