Fake Reviews Identification Based on Deep Computational Linguistic Features

Abstract

E-commerce platform has become an important resource of information. It takes into account the feedbacks of consumers about products and services purchased from the online website, these feedbacks are named as reviews. Online websites provide consumers with the ability to write product or service reviews after buying, so that when new customers make decisions to buy products or services from the online website, they read the recommendations or reviews written by people who have experienced the product or service. Those reviews, however, may be trusted (real) or spam (fake) reviews. E-commerce website fraudsters who deceive potential customers and reputation businesses or defame them can intentionally write fake reviews. Consequently, fake review detection techniques are essentially required for classification of reviews as fake (spam) or trusted (genuine) review. Main objective of this paper is to analyze, identify and detect the fake reviews of electronic products dataset that relate to different USA cities. In this paper, we investigate several feature extraction techniques such as LIWC, sentiment analysis, POS and subjectivity. Based on these methods, we extract set of features from the review text like authenticity, analytic thinking, polarity, objective, subjective, counts of adjective, verb, nouns and adverbs. For feature selection, we used an IG (Information Gain) to select discriminative and highest features.   Three different supervised machine-learning techniques are Decision tree, Random forest and Adaptive boosting are applied for classification the reviews as fake or trusted and the achieved results were 96 %, 94% and 97 % in the term of accuracy respectively.