Abstract
Whether purchasing a product or searching for a new doctor, consumers often turn to online reviews for recommendations. Determining whether reviews are truthful is imperative to the consumer, as to not get misled by false recommendations. Unfortunately, it is often difficult, or impossible, for humans to ascertain the validity of a review through reading the text, however, studies have shown machine learning methods perform well for detecting untruthful reviews. Previously, no studies have examined the effects of ensemble learners on the detection of untruthful reviews, despite these techniques being effective in related text classification domains. We seek to inform other researchers of the effects of ensemble techniques on the detection of spam reviews. To this aim, we evaluate four classifiers and three ensemble techniques using those four classifiers as base learners. We compare the results of Multinomial Naïve Bayes, C4.5, Logistic Regression, Support Vector Machine, Random Forest with 100, 250, and 500 trees, and Boosting and Bagging using the base learners. We found that none of the ensemble techniques tested were able to significantly improve review spam detection over standard Multinomial Naïve Bayes and thus, are not worth the computational expense they inflict.