[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-195216-105":3,"detail-sidebar-cat-1-en-105":81,"doc-detail-195216-en":127},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","deep-siamese-network-with-fuzzy-classifier-for-fake-review-detection","Deep Siamese Network with Fuzzy Classifier for Fake Review Detection","","This document details a novel approach to fake review detection using a deep Siamese network combined with a fuzzy classifier. The Siamese network architecture leverages both Word2Vec and BERT embeddings to learn sophisticated representations of review text. These representations are then fed into a fuzzy classifier, which employs membership layers and de-fuzzification for robust decision-making. The system aims to accurately distinguish between real and fake reviews. Performance evaluations are presented, alongside visualizations of the fuzzy logic output and word cloud examples from the dataset. The table on average review lengths for fake vs. real reviews across various product categories highlights differences that the proposed model seeks to exploit.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/template/","Template",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/template/general/","General",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/template/deep-siamese-network-with-fuzzy-classifier-for-fake-review-detection/195216/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/deep-siamese-network-with-fuzzy-classifier-for-fake-review-detection/195216.png","ImageObject",442,249,{"name":42,"@type":43},"Evangeline","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-09-28","2026-09-03",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",6,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the primary goal of the proposed system?","Question",{"text":63,"@type":64},"The primary goal of the proposed system is to accurately detect and distinguish between fake and real product reviews.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What types of embeddings are used in the Siamese network?",{"text":68,"@type":64},"The Siamese network utilizes both Word2Vec embeddings and BERT embeddings to capture nuanced semantic information from the review text.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the fuzzy classifier contribute to the detection process?",{"text":72,"@type":64},"The fuzzy classifier employs membership layers and de-fuzzification techniques to handle uncertainty and make more robust classification decisions, ultimately contributing to the overall accuracy of fake review detection.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},195216,1788446444,{"code":4,"msg":82,"data":83},"success",[84,89,94,99,104,109,114,119,124],{"id":85,"doc_module":22,"doc_module_name":25,"category_name":86,"show_sort_weight":87,"slug":88},11,"Presentations",90,"presentations",{"id":90,"doc_module":22,"doc_module_name":25,"category_name":91,"show_sort_weight":92,"slug":93},12,"Resumes",80,"resumes",{"id":95,"doc_module":22,"doc_module_name":25,"category_name":96,"show_sort_weight":97,"slug":98},14,"Invoices",70,"invoices",{"id":100,"doc_module":22,"doc_module_name":25,"category_name":101,"show_sort_weight":102,"slug":103},15,"Posters",60,"posters",{"id":105,"doc_module":22,"doc_module_name":25,"category_name":106,"show_sort_weight":107,"slug":108},16,"Social Media",50,"social-media",{"id":110,"doc_module":22,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},17,"Forms",40,"forms",{"id":115,"doc_module":22,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},18,"Letters",30,"letters",{"id":120,"doc_module":22,"doc_module_name":25,"category_name":121,"show_sort_weight":122,"slug":123},21,"Paper Templates",5,"papers-templates",{"id":125,"doc_module":22,"doc_module_name":25,"category_name":29,"show_sort_weight":4,"slug":126},158,"general-158",{"code":4,"msg":82,"data":128},{"doc_id":79,"user_id":129,"nickname":42,"user_avatar":130,"doc_module":22,"category_id":125,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":136,"language":137,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":138,"faqs":139,"seo_title":140,"seo_description":12,"update_tm":80,"read_time":30},13056703019662,"https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188","| Category |  | Fake |  | Real |  |\n| --- | --- | --- | --- | --- | --- |\n|  |  | No. of corpora | Average Length | No. of corpora | Average Length |\n| Books_5 |  | 2185 | 374 | 2185 | 491 |\n| Clothing_Shoes_and_Jewelry_5 Electronics_5 |  | 1924 | 250 | 1924 | 322 |\n|  |  | 1994 | 306 | 1994 | 408 |\n| Home_and_Kitchen_5 |  | 2028 | 272 | 2028 | 350 |\n| Kindle_Store_5 Movies_and_TV_5 |  | 2365 | 375 | 2365 | 474 |\n|  |  | 1794 | 335 | 1794 | 452 |\n| Pet_Supplies_5\u003Cbr>Sports_and_Outdoors_5 |  | 2127 | 276 | 2127 | 354 |\n|  |  | 1973 | 282 | 1973 | 363 |\n| Tools_and_Home_Improvement_Toys_and_Games_5 | 5 | 1929 | 292 | 1929 | 380 |\n|  |  | 1897 | 276 | 1897 | 357 |\n| Total Reviews |  | 20216 |  | 20216 |  |","cbCaiustYJgia3Et","https://ap.wps.com/l/cbCaiustYJgia3Et","pdf",1177437,8,"English","# Introduction\n## Methodology\n### Siamese Network Architecture\n### Fuzzy Classifier Design\n## Experimental Setup\n## Results and Discussion\n## Conclusion","[{\"question\":\"What is the primary goal of the proposed system?\",\"answer\":\"The primary goal of the proposed system is to accurately detect and distinguish between fake and real product reviews.\"},{\"question\":\"What types of embeddings are used in the Siamese network?\",\"answer\":\"The Siamese network utilizes both Word2Vec embeddings and BERT embeddings to capture nuanced semantic information from the review text.\"},{\"question\":\"How does the fuzzy classifier contribute to the detection process?\",\"answer\":\"The fuzzy classifier employs membership layers and de-fuzzification techniques to handle uncertainty and make more robust classification decisions, ultimately contributing to the overall accuracy of fake review detection.\"}]","Deep Siamese Network with Fuzzy Classifier for Fake Review Detection | PDF"]