Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Pally, Shelly Chelsy; Gokulapriya, R.; Jenefa, J. Title A name given to the resource Unmasking Fake Reviews: A Machine Learning Perspective Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1221;pp.13-23 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-0924-6_2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-0924-6_2</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105006876033?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105006876033?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Pally S.C., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India; Gokulapriya R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India; Jenefa J., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India Description An account of the resource In the age of online shopping and digital information, product reviews play a crucial role in influencing consumer decisions. Fake product reviews are widespread and can mislead consumers, affect brand reputation, distort market dynamics and necessitate the development of robust techniques to identify and combat fake reviews, which is the focus of this project. The advent of machine learning has transformed several industries, and its capability to combat the issue of fake product reviews is equally significant. The problem is because of fake previews customers which are in huge confusion for not knowing what to believe. The goal is to develop a predictive model that can differentiate between authentic and fraudulent product reviews. In this project to detect fake product reviews, three preprocessing methods, two feature extraction techniques, and six classifications algorithms are used. In this proposed system, we acquired better accuracy 88.93% using SVM. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Bag of words (Bow); NLP; Term frequency-inverse document frequency (TF-IDF) Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981960923-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online