Hybrid ensemble and soft computing approaches for review spam detection on different spam datasets

Amin, Irtiqa; Dubey, Mithilesh Kumar

doi:10.1016/j.matpr.2022.03.342

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…As a result, obtaining labelled data takes time and money, and the quality of training data must also be enhanced, which is a crucial step in developing accurate classi ers. The deployment of one or more competent staff agents is a standard strategy for obtaining classi ed data in most known current systems [25], [25], [26], [27] and [28] respectively.…”

Section: Challenges In Detecting Review Spammentioning

confidence: 99%

Combined novel soft computing and ensemble technique analysis based on Review Spam Detection

Amin

Dubey

Yaseen

et al. 2023

Preprint

Self Cite

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In the era of steadily expanding e-commerce, people prefer to purchase things and commodities online which helps them to save time and efforts. The huge quantity of these online purchase decisions is influenced by the suggestions of previous purchasers thereby increasing the spam content on the websites. As of the prevalence of spam content on social media is rapidly expanding, the subscribers receive a large number of junk information such as malevolent links, bogus accounts, fraud news and reviews via social networking sites and are unable to differentiate between spam and legitimate texts thereby making spam recognition vital. This research paper discusses about the novel soft computing and ensemble machine learning techniques and challenges based on detection of these review spams. It uses the datasets from different hotels and extracted its reviews, creation of novel model to analyze the problem, thus resulting in overall performance of model approximately equal to 85% of accuracy.

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Section: Challenges In Detecting Review Spammentioning

confidence: 99%

Combined novel soft computing and ensemble technique analysis based on Review Spam Detection

Amin

Dubey

Yaseen

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mostly, it is an effort to improve and enhance their businesses; service providers and online retailers might request their clients to give feedback regarding their experience with the services or products they bought and know if they are satisfied with the product or not [3]. Consumers might even feel inclined to review a service or product whenever they undergo a remarkably bad or good experience with those products or services [4]. While online reviews are helpful, trusting these reviews blindly becomes dangerous for buyers and sellers.…”

Section: Introductionmentioning

confidence: 99%

Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

Alwayle¹,

Al-onazi²,

Nour³

et al. 2023

Computer Systems Science and Engineering

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Online reviews regarding purchasing services or products offered are the main source of users' opinions. To gain fame or profit, generally, spam reviews are written to demote or promote certain targeted products or services. This practice is called review spamming. During the last few years, various techniques have been recommended to solve the problem of spam reviews. Previous spam detection study focuses on English reviews, with a lesser interest in other languages. Spam review detection in Arabic online sources is an innovative topic despite the vast amount of data produced. Thus, this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit (SRD-OSGRU) on Arabic Opinion Text. The presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes: spam and truthful. Initially, the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible format. Next, unigram and bigram feature extractors are utilized. The SGRU model is employed in this study to identify and classify Arabic spam reviews. Since the trial-and-error adjustment of hyperparameters is a tedious process, a white shark optimizer (WSO) is utilized, boosting the detection efficiency of the SGRU model. The experimental validation of the SRD-OSGRU model is assessed under two datasets, namely DOSC dataset. An extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.

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Machine Learning in Cybersecurity: Evaluating Text Encoding Techniques for Optimized SMS Spam Detection

Filali,

Alaoui,

Merras

2024

Lecture Notes in Networks and Systems

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Hybrid ensemble and soft computing approaches for review spam detection on different spam datasets

Cited by 6 publications

References 6 publications

Combined novel soft computing and ensemble technique analysis based on Review Spam Detection

Combined novel soft computing and ensemble technique analysis based on Review Spam Detection

Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

Machine Learning in Cybersecurity: Evaluating Text Encoding Techniques for Optimized SMS Spam Detection

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