Revisiting Semi-Supervised Learning for Online Deceptive Review Detection

Rout, Jitendra Kumar; Dalmia, Anmol; Choo, Kim‐Kwang Raymond; Bakshi, Sambit; Jena, Sanjay Kumar

doi:10.1109/access.2017.2655032

Cited by 96 publications

(40 citation statements)

References 36 publications

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“…Set up a control group for experiments to prove the effectiveness and portability of this method. A variety of methods were tested on two different datasets, one using YelpChi dataset (5854 pieces of data), the other using YelpRes dataset (15,141 pieces of data), and adjusting the experimental results obtained by adjusting the optimal parameters of each Semi-supervised [45]: a kind of semi-supervised learning, based on a single classifier for reinforcement learning, the classifier chooses RF;…”

Section: ) Comparative Experimentsmentioning

confidence: 99%

Fake Review Detection Based on Multiple Feature Fusion and Rolling Collaborative Training

et al. 2020

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Fake reviews may mislead consumers. A large number of fake reviews will even cause huge property losses and public opinion crises. Therefore, it is necessary to detect and filter fake reviews. However, most existing methods have lower accuracy in detecting fake reviews due to they just use single features and lack of labeled experimental data. To solve this problem, we propose a novelty method to detect fake reviews based on multiple feature fusion and rolling collaborative training. First, the method requires an initial index system with multiple features such as text features, sentiment features of reviews and behavior features of reviewers. Second, the method needs an initial training sample set. Thus, we designed related algorithms to extract all the features of a review. Then the classification of the review is labeled manually. Finally, the method uses the initial sample set to train 7 classifiers, and the most accurate classifier will be selected to classify new reviews. The novelty of the method lies in that the features and the classification labels of the new reviews will be added into the initial sample set as new samples. So the size of the sample set will increase automatically. The experimental results in the reviews of yelp shopping website show that the accuracy of the proposed method for detecting fake reviews is 84.45%, which is 3.5% higher than the baseline methods. And compared with the latest deep learning model, its baseline precision has increased by 5.3%. According to the Friedman test, the support vector machine (SVM) classifier and random forest (RF) classifier has been proven to be the best one by statistical means. It means our method which uses multiple features has higher accuracy than the baseline models. Meanwhile, it also resolves the problem of lacking labeled training samples in fake reviews detection. INDEX TERMS Fake review detection, machine learning, multiple feature fusion, feature extraction, rolling collaborative training

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Section: ) Comparative Experimentsmentioning

confidence: 99%

Fake Review Detection Based on Multiple Feature Fusion and Rolling Collaborative Training

et al. 2020

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“…It has been observed from the literature that Spam Review detection using linguistic method uses only review text for spotting the spam review [37], [38]. It is usually performed binary classification in which the review is classified as ''spam'' or ''not spam''.…”

Section: Spam Review Detection Using Linguistic Methods (Srd-lm)mentioning

confidence: 99%

Spam Review Detection Using the Linguistic and Spammer Behavioral Methods

et al. 2020

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Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profits or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, the spam review detection problem has gained much attention from communities and researchers, but still there is a need to perform experiments on real-world large-scale review datasets. This can help to analyze the impact of widespread opinion spam in online reviews. In this work, two different spam review detection methods have been proposed: (1) Spam Review Detection using Behavioral Method (SRD-BM) utilizes thirteen different spammer's behavioral features to calculate the review spam score which is then used to identify spammers and spam reviews, and (2) Spam Review Detection using Linguistic Method (SRD-LM) works on the content of the reviews and utilizes transformation, feature selection and classification to identify the spam reviews. Experimental evaluations are conducted on a real-world Amazon review dataset which analyze 26.7 million reviews and 15.4 million reviewers. The evaluations show that both proposed models have significantly improved the detection process of spam reviews. Specifically, SRD-BM achieved 93.1% accuracy whereas SRD-LM achieved 88.5% accuracy in spam review detection. Comparatively, SRD-BM achieved better accuracy because it works on utilizing rich set of spammers behavioral features of review dataset which provides in-depth analysis of spammer behaviour. Moreover, both proposed models outperformed existing approaches when compared in terms of accurate identification of spam reviews. To the best of our knowledge, this is the first study of its kind which uses large-scale review dataset to analyze different spammers' behavioral features and linguistic method utilizing different available classifiers. INDEX TERMS Online product reviews, spam reviews, spam review detection, linguistic features, spammer behavioral features.

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“…The datasets utilized in certain assessments was "more extravagant" than recently utilized dataset as in it contains surveys with both positive and negative feelings. The spam detection is done usually done using minimal meta-data which is more complex [1]. The spam detection is even done in short messaging service(SMS).…”

Section: Related Workmentioning

confidence: 99%

Detecting Online Spams through Supervised Learning Techniques

Minu¹,

Mounika²,

Suhasini³

et al. 2019

IJITEE

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With more customers utilizing on the online review surveys to educate their administration basic leadership, assessment of reviews which economically affect the reality of organizations. Obviously, crafty people or gatherings have endeavored to manhandle or control online review spam to make benefits, etc, and that tricky recognition and counterfeit sentiment surveys is a subject of continuous research intrigue. In this paper, we clarify how supervised learning strategies are utilized to recognize online spam review surveys, preceding showing its utility utilizing an informational index of lodging reviews

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Revisiting Semi-Supervised Learning for Online Deceptive Review Detection

Cited by 96 publications

References 36 publications

Fake Review Detection Based on Multiple Feature Fusion and Rolling Collaborative Training

Fake Review Detection Based on Multiple Feature Fusion and Rolling Collaborative Training

Spam Review Detection Using the Linguistic and Spammer Behavioral Methods

Detecting Online Spams through Supervised Learning Techniques

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