Unmasking Fake Reviews: A Machine Learning Perspective

Title

Unmasking Fake Reviews: A Machine Learning Perspective

Creator

Pally, Shelly Chelsy; Gokulapriya, R.; Jenefa, J.

Description

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.

Source

Lecture Notes in Networks and Systems;Volume;1221;pp.13-23

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Bag of words (Bow); NLP; Term frequency-inverse document frequency (TF-IDF)

Coverage

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

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981960923-9;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-0924-6_2

https://www.scopus.com/pages/publications/105006876033?origin=resultslist