Abstract
Due to the advancements of Internet, majority of people will prefer to buy and sell the commodities through e-commerce websites. In general, people mostly trust on reviews before taking the decisions. Fraudulent reviewers will consider this as an opportunity to write fake reviews for misleading both the customers and producers. There is a necessity to identify fake reviews before making it available for decision making. This research focuses on fake review detection by using content-related features, which includes linguistic features, POS features, and sentiment analysis features. Ontology-based method is used for performing the aspect-wise sentiment analysis. All the features of reviews are calculated and incorporated into the ontology, and fake review detection is also accelerated through the rule-based classifier by inferencing and querying the ontology. Due to the issues related with labeled dataset, the outliers from an unlabeled dataset were selected as fake reviews. The performance measure of the rule-based classifier outperforms by incorporating all the content-related features.KeywordsOntologyRule-based classifierOutliersContent-related features
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