Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning

Zhong, Minjuan; Li, Zhenjin; Liu, Shengzong; Yang, Bo; Tan, Rui; Qu, Xiaobo

doi:10.1155/2021/9923374

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…The article [ 12 ] introduces semi supervised learning of detection models for spam reviews. Machine learning methods were combined with time series.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data

Machová

Mach

Vasilko

2021

Sensors

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The article focuses on solving an important problem of detecting suspicious reviewers in online discussions on social networks. We have concentrated on a special type of suspicious authors, on trolls. We have used methods of machine learning for generation of detection models to discriminate a troll reviewer from a common reviewer, but also methods of sentiment analysis to recognize the sentiment typical for troll’s comments. The sentiment analysis can be provided also using machine learning or lexicon-based approach. We have used lexicon-based sentiment analysis for its better ability to detect a dictionary typical for troll authors. We have achieved Accuracy = 0.95 and F1 = 0.80 using sentiment analysis. The best results using machine learning methods were achieved by support vector machine, Accuracy = 0.986 and F1 = 0.988, using a dataset with the set of all selected attributes. We can conclude that detection model based on machine learning is more successful than lexicon-based sentiment analysis, but the difference in accuracy is not so large as in F1 measure.

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“…The article [ 12 ] introduces semi supervised learning of detection models for spam reviews. Machine learning methods were combined with time series.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data

Machová

Mach

Vasilko

2021

Sensors

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“…The primary objective is to develop accurate methodologies that analyze reviews on e-commerce platforms, such as Amazon, by strengthening feature extraction techniques across various models. By leveraging advanced machine learning algorithms and feature extraction methods, it becomes possible to identify fake reviews more effectively (7)(8)(9) .…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Feature Extraction Technique and Spam Review Detection using Ensemble Machine Learning Algorithm by Web Scrapping

Goyal¹,

Pal²,

Keswani³

et al. 2023

IJST

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Objectives: To develop a novel hybrid method for feature generation and a novel dataset for experimenting and extracting the features for numerical representation. Methods: In the pursuit of the best spam review detection model, a four-stage process was undertaken. Initially, a dataset 'Fake reviews' was collected from Flipkart, containing 9926 samples from the home and kitchen products domain. Next, the data underwent pre-processing using the Natural Language Toolkit (NLTK) library. A novel Hybrid Feature Generator (HFG) was then developed, extracting informative features based on parameters like TF-IDF (Term Frequency -Inverse Document Frequency), sentiment analysis scores, and syntactic patterns. Finally, the model was trained on these generated features using Gaussian Naïve Bayes (GNB), Multinomial Naïve Bayes (MNB), and Bernoulli Naïve Bayes (BNB) algorithms. Performance evaluation was conducted using metrics such as accuracy, precision, recall, and F1-score, comparing the model's results to gold standard or known spam reviews. Findings: The feature generation technique was implemented on three different models, and the models were trained using 70% of the available data. The results of these experiments showed that GNB, NB, and NB achieved testing accuracies of 99.7%, 96.4%, and 99%, respectively. The performance of these models was compared with and without the inclusion of extracted product review features. The results demonstrated that the GNB algorithm outperformed the other methods in terms of accuracy and precision. Novelty: This study presents a novel HFG for feature extraction from review-text and a novel dataset that outperforms hitherto reported https://www.indjst.org/ approaches.

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Deceptive opinion spam detection using bidirectional long short-term memory with capsule neural network

Shinde,

Pawar,

Jagtap

et al. 2023

Multimed Tools Appl

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Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning

Cited by 4 publications

References 24 publications

Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data

Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data

A Novel Hybrid Feature Extraction Technique and Spam Review Detection using Ensemble Machine Learning Algorithm by Web Scrapping

Deceptive opinion spam detection using bidirectional long short-term memory with capsule neural network

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