Detecting and Characterizing Extremist Reviewer Groups in Online Product Reviews

Gupta, Viresh; Aggarwal, Aayush; Chakraborty, Tanmoy

doi:10.1109/tcss.2020.2988098

Cited by 21 publications

(11 citation statements)

References 44 publications

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“…Mukherjee et al [14] used cosine similarity; however, different greater superior similarity capabilities primarily based totally upon phrase meanings as opposed to the phrases themselves have proven promise [8]. Similarly, reviewer-centric features for groups are included in the paper by Gupta et al [1], in which they have collected lakhs of reviews on brands from the Amazon product review site and manually labeled a set of 923 candidate reviewers groups.…”

Section: E Maximum Content Similaritymentioning

confidence: 99%

“…Surprisingly, the authors observed that there are a lot of verified reviewers showing extreme sentiment, which, on further investigation, leads to ways to avoid the current mechanisms in place to prevent unofficial encouragements on Amazon. Various features, in brief, are mentioned for detection of a group of fake reviewers [1]: F. Average Rating It is used to captures the average rating given by group G to a certain brand B. It does the average of the reviews given by group members to products of the given brand and takes the mean of these ratings.…”

Section: E Maximum Content Similaritymentioning

confidence: 99%

“…The literature described in this paper focuses primarily on feature engineering, Feature Engineering studies give a brief idea of features mainly used for reviewer-centric spam detection. The main goal of this paper to focus on Reviewer-centric features is because detection of one fake reviewer can potentially help to identify a number of fake reviews hence it has proven to be important for accurate detection of review spam, as shown in [1,10,12,13,14,15]. Despite many studies focused on feature engineering, experiments use different datasets, so it is not possible to identify the optimal type of feature.…”

Section: R Reviewer Burst (Rb)mentioning

confidence: 99%

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Survey on Comprehensive Study of Fake Reviews and Reviewers Detection using machine learning techniques

Lahire¹

2022

IJRASET

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Individuals and businesses are increasingly using opinionated social media, such as product evaluations, to make decisions. People, however, try to game the system for profit or fame by opinion spamming (e.g., creating bogus reviews) to promote or demote certain specific items. Such bogus reviews should be identified in order for reviews to reflect real user experiences and opinions. Most of the consumers are influenced by the online reviews on the product and it plays a crucial role in finalizing purchase decisions in the market. But fake reviewers or spammers misused and take advantage by writing fake reviews, positive fake reviews to promote the product, or negative fake reviews to demote the product. There has been huge research in this domain for more than a decade for detecting fake reviews or fake reviewers. Howsoever many fake reviewers work together by creating groups to target any product and writing fake reviews on product in bulk, reviewers create multiple fake IDs and write fake reviews. Detecting false reviews and specific fraudulent reviewers was the subject of previous work on opinion spam. The primary thing of this study is to give a strong and comprehensive relative study for detecting fake reviews and reviewers using machine learning. Keywords: Fake review, Fake reviewers Spam opinion, Opinion mining, FIM, Reviewer-centric spam, feature engineering

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Section: E Maximum Content Similaritymentioning

confidence: 99%

Section: E Maximum Content Similaritymentioning

confidence: 99%

Section: R Reviewer Burst (Rb)mentioning

confidence: 99%

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Survey on Comprehensive Study of Fake Reviews and Reviewers Detection using machine learning techniques

Lahire¹

2022

IJRASET

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“…The four convlotuion operations proposed in this work perform low on English emotions. Gupta et al [10] proposed a feature-based supervised model to identify the extremist reviewers who target whole brand. The datasets are created by crawling reviews from Amazon website.…”

Section: Related Workmentioning

confidence: 99%

Rider Driven African Vulture Optimization with Multi Kernel Structured Text Convolutional Neural Network for Classifying e-Commerce Reviews

Zakir¹,

Jinny²

2022

IJACSA

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Opinion mining is a natural language processing based on sentiment classification technique to determine the sentiment of the reviews. The major existing text Convolutional Neural Network (CNN) algorithms are derived based on 𝟑 × 𝟑 size kernels which extract ineffective review text-features and lead to less classification accuracy. Moreover, most of the traditional CNN versions output three classes such as positive, negative, and neutral as their classification results. Hence, a novel algorithm namely 'RAVO driven Multi-Size Kernel structured Text CNN for classifying ecommerce reviews (MSK-TCNN-RAVO)' is proposed in this work. This proposed approach utilizes five multi-size kernels (𝟑 × 𝟕,𝟓 × 𝟕,𝟏 × 𝟑,𝟏 × 𝟓,𝟏 × 𝟕), multi-dimensional kernels (1D & 2D), and integrates varying size kernels to extract text-features effectively.In addition, the performance of multi-kernel CNN is highly enhanced by RAVO algorithm based on rider optimization. Moreover, the proposed approach is highly effective to process 'review-stop-words removal' that decrease the complexity and time consumption of the opinion mining process. Most existing systems use single pooling operations which reduce feature map processing performance, hence, dual pooling operations (both Max and Average pooling) are employed in this research. Furthermore, it is configured to generate five classification outputs such as bad, fair, neutral, good, and excellent to support better decisionmaking with 95.5% accuracy. This method is evaluated using different quality metrics and five review-databases to measure the performance, and the results reveal that the proposed method outperforms the other existing review classification algorithms.

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“…Gupta et al 30 detected and characterized the extremist reviewer groups based on online product reviews. The algorithms employed for detecting the review of online products were a feature-based supervised model, three-layer perceptron-based classifier.…”

Section: Review Of Related Workmentioning

confidence: 99%

Fuzzy artificial bee colony‐based CNN‐LSTM and semantic feature for fake product review classification

Jacob¹,

Rajendran²

2021

Concurrency and Computation

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In recent years, online reviews are considered as the most significant resource for consumers to make a decision regarding the purchase of a particular product. The reviews can either encourage or relegate a product; therefore posting fake reviews has turned into a money-spinning business in the modern period. The detection of fake reviews has become a center of attraction for various business people. This research study aims in detecting fake product reviews using four significant phases namely the data pre-processing, feature extraction, feature selection, and classification. The features obtained in the pre-processing phase are extracted and selected using chi-squared technique to obtain a delegate subset among all data and to reduce the complication issues. Then a CNNLSTM-FABC approach classifies and detects the review as fake or real. Finally, the performance evaluation and the comparative analysis are carried out to determine the effectiveness of the proposed approach. The results reveal that the proposed approach performs well irrespective of the product type and sentiment polarity.

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Detecting and Characterizing Extremist Reviewer Groups in Online Product Reviews

Cited by 21 publications

References 44 publications

Survey on Comprehensive Study of Fake Reviews and Reviewers Detection using machine learning techniques

Survey on Comprehensive Study of Fake Reviews and Reviewers Detection using machine learning techniques

Rider Driven African Vulture Optimization with Multi Kernel Structured Text Convolutional Neural Network for Classifying e-Commerce Reviews

Fuzzy artificial bee colony‐based CNN‐LSTM and semantic feature for fake product review classification

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