[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124233-en":3,"doc-seo-124233-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124233,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Comparative Analysis of Machine Learning Models for Detecting Fake Reviews on Amazon","This master’s thesis evaluates the efficiency of machine learning models for detecting fake reviews on Amazon in response to the rapid growth of e-commerce and the resulting erosion of consumer trust. It compares Logistic Regression, Support Vector Machines, Random Forest, and Gradient Boosting using a publicly available dataset with review text and reviewer metadata. After text cleaning, tokenization, and feature extraction, performance is assessed with accuracy, precision, recall, and F1 score. Findings show ensemble approaches, especially Random Forest and Gradient Boosting, achieve stronger overall classification and recall while highlighting the need to address imbalanced data and improve model transparency and interpretability.","Krishna Patel  \nComparative Analysis of Machine Learning Models for Detecting Fake Reviews on Amazon  \nMetropolia University of Applied Sciences Master of Engineering  \nInformation Technology  \nMaster’s Thesis  \n1 January 2025  \nPREFACE  \nAs an android application developer working on this topic was quite challenging but more rewarding. During coursework of Artificial Intelligence with Python I found interest in AI and decided to go deep diving in the AI era.  \nIn the course of this study, I examined the use of machine learning models for detecting fake reviews on Amazon. Through the comparison of different machine learning algorithms such as Logistic Regression, Support Vector Machines (SVM), Random Forest and Gradient Boosting, I hope to find out which of the models will prove to be the most efficient when it comes to discriminating between genuine and fake reviews.  \nWith this research, I aim to make a contribution to this field and apply the machine learning techniques to find the deceptive patterns and increase the review authenticity. Overall, I want to help make online reviews trustworthy and give businesses a set of tools to fight the fraud with the reviews.  \nI am very grateful to my thesis instructure Toni Spännäri, who provided guidance, support, and thoughtful feedback throughout this work and encouraged me to complete within a certain deadline. Many thanks to Ville Jääskeläinen for helping me finalize the thesis topic.  \nFinally, my heartiest thanks from the bottom of my heart to my family members, especially my daughter(Niyati Patel), my husband, parents and in-laws for their support, patience and motivation throughout this study. Their understanding during the late nights and busy weekends helped me finish this thesis.  \nEspoo, 25 May 2025  \nKrishna Patel  \nAbstract  \nAuthor: Krishna Patel  \nTitle: Comparative Analysis of Machine Learning Models for  \nDetecting Fake Reviews on Amazon  \nNumber of Pages: 56 pages + 9 appendices  \nDate: 1 January 2025  \nDegree: Master of Engineering  \nDegree Programme: Information Technology  \nProfessional Major: Networking and Services  \nSupervisor: Toni Spännäri, Senior Lecturer  \nThis research is concerned with the efficiency of machine learning models when it comes to detecting fake reviews on Amazon. Since or because of the rapid growth of e-commerce, online reviews have become important in determinations of consumer decisions. But the growing trend of fake reviews erodes the trust of the consumers and alters the behavior of the market. There are various evaluations of machine learning algorithms: Logistic Regression, Support Vector Machines (SVM), Random Forest, and Gradient Boosting among others to establish the most effective and reliable model for detection of fake reviews.  \nThis study uses a publicly available dataset of Amazon product reviews identified as either genuine or fake, which contains text data as well as metadata on reviewers. Before training the models, methods of data preprocessing are applied, including the text cleaning, tokenization, and feature extraction. Performance evaluation is done based on the metrics of accuracy, precision, recall, and F1 score. Results reveal that the ensemble methods such as Random Forest, and Gradient Boosting classifiers perform better than other models in terms of recall as well as overall classification performance. The study identifies the issues with processing imbalanced datasets and points to its importance to pay attention to model transparency and interpretability. Lastly, the research offers recommendations to e-commerce platforms in order to increase the review credibility and safeguard consumer trust.  \nKeywords: ML, Amazon, review detection, LR, SVM, RF, GB, E-commerce, text categorization, data preprocessing, accuracy, recall, precision, F1 score, model assessment, dataset, genuineness of reviews, trust of the consumers.  \nContents  \nList of Abbreviations  \n1 Introduction 1  \n1.1 Background of Online Reviews 1  \n","cbCaivfM3r3wkiHL","https://ap.wps.com/l/cbCaivfM3r3wkiHL","pdf",2787989,1,75,"English","en",105,"# Introduction\n## Background of Online Reviews\n## Research Problem\n## Research Objectives\n## Scope and Limitations\n# Literature Review\n## Evolution of Review Analysis\n## Existing Fake Review Detection Techniques\n## Machine Learning Models in Review Analysis\n## Theoretical Framework\n# Research Methodology\n## Research Design\n## Data Collection\n## Data Preprocessing\n## Machine Learning Models\n## Feature Engineering\n## Model Evaluation Metrics\n## Ethical Considerations in Fake Review Detection\n## Chapter Summary\n# Results and Analysis\n## Overview\n## Preprocessing\n## Exploratory Data Analysis\n## Machine Learning Model\n## Model Performance Comparison\n## Chapter Summary\n# Discussions and Conclusions\n## Interpretation of Results\n## Practical Implications\n## Model Performance Insights\n# Summary\n## Research Summary\n## Limitations\n## Future Research Directions","[{\"question\":\"Which machine learning models are compared for fake review detection on Amazon?\",\"answer\":\"The study compares Logistic Regression, Support Vector Machines (SVM), Random Forest, and Gradient Boosting classifiers to determine which model performs best for distinguishing genuine versus fake reviews.\"},{\"question\":\"How does the thesis prepare the dataset before training models?\",\"answer\":\"It applies data preprocessing steps such as text cleaning, tokenization, and feature extraction using the review text and reviewer metadata from a publicly available labeled dataset.\"},{\"question\":\"What evaluation metrics are used to assess model performance?\",\"answer\":\"Model performance is evaluated using accuracy, precision, recall, and F1 score.\"}]","Comparative Analysis of Machine Learning Models for Detecting Fake Reviews on Amazon | 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