Ontology based sentiment analysis for fake review detection

Majority of customers and manufacturers who tend to purchase and trade via e-commerce websites primarily rely on reviews before making purchasing decisions and product improvements. Deceptive reviewers consider this opportunity to write fake reviews to mislead customers and manufacturers. This calls for the necessity of identifying fake reviews before making them available for decision making. Accordingly, this research focuses on a fake review detection method that incorporates review-related features including linguistic features, Part-of-Speech (POS) features, and sentiment analysis features. A domain feature ontology is used in the feature-level sentiment analysis and all the review-related features are extracted and integrated into the ontology. The fake review detection is enhanced through a rule-based classifier by inferencing the ontology. Due to the lack of a labeled dataset for model training, the Mahalanobis distance method was used to detect outliers from an unlabeled dataset where the outliers were selected as fake reviews for model training. The performance measures of the rule-based classifier were improved by integrating linguistic features, POS features, and sentiment analysis features, in spite of considering them separately.

Automated summary

A majority of customers and manufacturers who engage in e-commerce rely on reviews for making purchasing decisions. However, fake reviews are problematic as they mislead decision-making. This research proposes a method for detecting fake reviews by integrating linguistic features, Part-of-Speech (POS) features, and sentiment analysis features. The detection is improved through the use of a rule-based classifier and a domain feature ontology. The performance measures of the classifier are enhanced by considering the features together rather than separately.