[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127054-en":3,"doc-seo-127054-105":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},127054,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Hotel Reviews Analysis Using Machine Learning Algorithms and Text Mining Model - read online free","Digital decision-making increasingly relies on online hotel reviews, making review authenticity critical in business and commerce. The study addresses the rise of fake reviews created by paid writers or automated text-generation systems that aim to manipulate user perceptions. Semi-supervised and supervised text mining approaches are developed to identify deceptive reviews, and their performance is compared on datasets containing hotel reviews and online feedback. Results focus on improving trustable customer information for transparent marketplaces.","Journal of Science and Technology  \nISSN: 2456-5660 Volume 8, Issue 12 (Dec-2023)  \n[www.jst.org.in](www.jst.org.in) DOI:[https://doi.org/10.46243/jst.2023.v8.i12.pp31-39](https://doi.org/10.46243/jst.2023.v8.i12.pp31-39)  \nHotel Reviews Analysis Using Machine Learning Algorithms and Text Mining Model  \nLekha Sri¹, Aman Kumar Piyush², Puli Vikram³, D Kalpana⁴  \n1,2,3 B.Tech Student, Department of Emerging Technologies (Cyber Security) from Malla Reddy College of  \nEngineering and Technology, Hyderabad, India.  \n⁴Assistant Professor, Department of Emerging Technologies (Cyber Security) from Malla Reddy College of  \nEngineering and Technology, Hyderabad, India.  \nTo Cite this Article  \nLekha Sri¹, Aman Kumar Piyush², Puli Vikram³, D Kalpana,“ Hotel Reviews Analysis Using Machine Learning Algorithms and Text Mining Model” Journal of Science and Technology, Vol. 08, Issue 12,- Dec 2023, pp23-30  \nArticle Info  \nReceived: 12-11-2023 Revised: 22-11-2023 Accepted: 02-12-2023 Published: 12-12-2023  \nAbstract-In the era of digital decision-making, where consumers heavily rely on online reviews, the authenticity of these reviews becomes paramount. However, the proliferation of fake reviews poses a significant challenge. In response, this study introduces and analyzes machine learning algorithms dedicated to discerning genuine feedback from deceptive ones within the context of hotel reviews and Online reviews have a significant impact on today‘s business and commerce. Decision-making for the purchase of online products mostly depends on reviews given by the users. Hence, opportunistic individuals or groups try to manipulate product reviews for their interests. This paper introduces some semi-supervised and supervised text mining models to detect fake online reviews as well as compares the efficiency of both techniques on datasets containing hotel reviews.  \nKeywords-Hotel Review, Text Mining, Machine Learning, Algorithms, Naive Bayes, Supervised and semi-supervised.  \nI. INTRODUCTION  \nIn today's digital age, the internet is a vast arena where customers freely express their opinions through reviews, significantly impacting businesses and guiding future consumers in their decision-making process. The surge in customer reviews witnessed in recent years has made them an invaluable resource for individuals seeking insights into products or services before making a choice.  \nThese reviews wield considerable influence, shaping the decisions of potential buyers. The power of social media amplifies this influence, as customers perusing reviews on platforms determine whether to proceed with a purchase or reconsider their choices. Positive reviews translate to financial gains for businesses, while negative ones can have adverse effects. Customers, thus, hold a pivotal role in reshaping businesses by providing feedback that enhances products, services, and marketing strategies.  \nJournal of Science and Technology  \nISSN: 2456-5660 Volume 8, Issue 12 (Dec-2023)  \n[www.jst.org.in](www.jst.org.in) DOI:[https://doi.org/10.46243/jst.2023.v8.i12.pp31-39](https://doi.org/10.46243/jst.2023.v8.i12.pp31-39)  \nHowever, amidst the genuine feedback, a shadow looms—the challenge of fake reviews. These deceptive evaluations can be produced through human-generated means, where content creators are paid to craft authentic-appearing but fictitious reviews. Alternatively, automated processes driven by text-generation algorithms have become increasingly prevalent. Technological advancements in natural language processing (NLP) and machine learning (ML) have facilitated the automation of fake reviews, creating them at scale and a fraction of the cost compared to their human-generated counterparts.  \nThe significance of addressing fake reviews is underscored by scholarly contributions such as Wu et al.'s conceptual framework, which outlines an agenda for investigating fake reviews. Their work sheds light on the antecedents, consequences, and interventions in understandi","cbCaikm9KoZ40EBm","https://ap.wps.com/l/cbCaikm9KoZ40EBm","pdf",614939,1,9,"English","en",105,"# Introduction\n## Fake reviews and their impact\n# Literature Review\n## Methods for detecting deceptive reviews","[{\"question\":\"Why are authentic hotel reviews important in online decision-making?\",\"answer\":\"Customers use online reviews to guide purchase choices, and review sentiment can directly affect business outcomes. Authentic feedback supports trustworthy decisions and better marketing and product development.\"},{\"question\":\"What techniques does the study use to detect fake reviews?\",\"answer\":\"The study introduces semi-supervised and supervised text mining models for identifying deceptive hotel reviews. It also compares the efficiency of both techniques on hotel review datasets.\"},{\"question\":\"What are the main sources of fake reviews mentioned in the paper?\",\"answer\":\"Fake reviews may be human-generated by paid content creators or generated at scale by automated text-generation algorithms. Advances in NLP and machine learning enable inexpensive production of deceptive reviews.\"}]","Hotel Reviews Analysis Using Machine Learning Algorithms and Text Mining Model - read online free | PDF",1785936566,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"hotel-reviews-analysis-using-machine-learning-algorithms-and-text-mining-model-read-online-free","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/hotel-reviews-analysis-using-machine-learning-algorithms-and-text-mining-model-read-online-free/127054/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are authentic hotel reviews important in online decision-making?","Question",{"text":75,"@type":76},"Customers use online reviews to guide purchase choices, and review sentiment can directly affect business outcomes. Authentic feedback supports trustworthy decisions and better marketing and product development.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What techniques does the study use to detect fake reviews?",{"text":80,"@type":76},"The study introduces semi-supervised and supervised text mining models for identifying deceptive hotel reviews. It also compares the efficiency of both techniques on hotel review datasets.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main sources of fake reviews mentioned in the paper?",{"text":84,"@type":76},"Fake reviews may be human-generated by paid content creators or generated at scale by automated text-generation algorithms. 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