Abstract The impact of online reviews on businesses has grown significantly during last years, being crucial to determine business success in a wide array of sectors, ranging from restaurants, hotels to e-commerce. Unfortunately, some users use unethical means to improve their online reputation by writing fake reviews of their businesses or competitors. Previous research has addressed fake review detection in a number of domains, such as product or business reviews in restaurants and hotels. However, in spite of its economical interest, the domain of consumer electronics businesses has not yet been thoroughly studied. This article proposes a feature framework for detecting fake reviews that has been evaluated in the consumer electronics domain. The contributions are fourfold: (i) Construction of a dataset for classifying fake reviews in the consumer electronics domain in four different cities based on scraping techniques; (ii) definition of a feature framework for fake review detection; (iii) development of a fake review classification method based on the proposed framework and (iv) evaluation and analysis of the results for each of the cities under study. We have reached an 82 F-Score on the classification task and the Ada Boost classifier has been proven to be the best one by statistical means according to the Friedman test.
Textual features have been proposed in several papers. @cite_20 employed psycholinguistic features based on LIWC @cite_19 combined with standard word and POS n-gram features. @cite_21 extend that work including also style and POS based features, such as deep syntax and POS sequence patterns. However, the detection of fake reviews based only on textual features is challenging. Other articles propose additional textual features such as semantic similarity and emotion @cite_24 , a wide variety of lexical and syntactic features @cite_48 and deeper details such as understandability, level of details, writing style and cognition indicators @cite_36 .
@cite_36 @cite_48 @cite_21 @cite_24 @cite_19 @cite_20
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Before making a purchase, users are increasingly inclined to browse online reviews that are posted to share post-purchase experiences of products and services. However, not all reviews are necessarily authentic. Some entries could be fake yet written to appear authentic. Conceivably, authentic and fake reviews are not easy to differentiate. Hence, this paper uses supervised learning algorithms to analyze the extent to which authentic and fake reviews could be distinguished based on four linguistic clues, namely, understandability, level of details, writing style, and cognition indicators. The model performance was compared with two baselines. The results were generally promising. The services and products of E-Commerce portals in this digital age are heavily reviewed by the users. These reviews provide useful insights on the quality usage of these products. Due to such importance of reviews, they can be faked to give false opinions about products and subsequently mislead the users. In this paper we are proposing new set of lexical and syntactic features set and applying supervised algorithms for performing classification on fake reviews dataset (gold standard). We focus on the writing style, that include type of punctuation mark, Part-of- Speech (POS) etc., that are helpful for detection of reviews spam. The final results give promising accuracy 91.51 for detecting fake reviews. Online reviews have become a valuable resource for decision making. However, its usefulness brings forth a curse ‒ deceptive opinion spam. In recent years, fake review detection has attracted significant attention. However, most review sites still do not publicly filter fake reviews. Yelp is an exception which has been filtering reviews over the past few years. However, Yelp’s algorithm is trade secret. In this work, we attempt to find out what Yelp might be doing by analyzing its filtered reviews. The results will be useful to other review hosting sites in their filtering effort. There are two main approaches to filtering: supervised and unsupervised learning. In terms of features used, there are also roughly two types: linguistic features and behavioral features. In this work, we will take a supervised approach as we can make use of Yelp’s filtered reviews for training. Existing approaches based on supervised learning are all based on pseudo fake reviews rather than fake reviews filtered by a commercial Web site. Recently, supervised learning using linguistic n-gram features has been shown to perform extremely well (attaining around 90 accuracy) in detecting crowdsourced fake reviews generated using Amazon Mechanical Turk (AMT). We put these existing research methods to the test and evaluate performance on the real-life Yelp data. To our surprise, the behavioral features perform very well, but the linguistic features are not as effective. To investigate, a novel information theoretic analysis is proposed to uncover the precise psycholinguistic difference between AMT reviews and Yelp reviews (crowdsourced vs. commercial fake reviews). We find something quite interesting. This analysis and experimental results allow us to postulate that Yelp’s filtering is reasonable and its filtering algorithm seems to be correlated with abnormal spamming behaviors. As people are spending more time to shop and view reviews on line, some reviewer write fake reviews to earn credit and to promote (demote) the sales of product and stores. Detecting fake reviews and spammers becomes more important when the spamming behavior is becoming damaging. This paper proposes three types of new features which include review density, semantic and emotion and gives the model and algorithm to construct each feature. Experiments show that the proposed model, algorithm and features are efficient in fake review detection task than traditional method based on content, reviewer info and behavior. We are in the midst of a technological revolution whereby, for the first time, researchers can link daily word use to a broad array of real-world behaviors. This article reviews several computerized text analysis methods and describes how Linguistic Inquiry and Word Count (LIWC) was created and validated. LIWC is a transparent text analysis program that counts words in psychologically meaningful categories. Empirical results using LIWC demonstrate its ability to detect meaning in a wide variety of experimental settings, including to show attentional focus, emotionality, social relationships, thinking styles, and individual differences. Consumers increasingly rate, review and research products online. Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam---fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90 accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.