Employing Semi-Supervised and Supervised Learning to  Discover False Online Ratings

Giribabu Sadineni; Reddy D Janardhan; Ch Meghana Sri; K Deepthi; M Kaveri; J Aiswarya

doi:10.55524/ijircst.2023.11.3.18

Authors

Giribabu Sadineni Assocate Professor, Department of Computer Science and Engineering, PACE Institute of Technology and Sciences, Ongole, Andhra Pradesh, India Author
Reddy D Janardhan Asstistant Professor, Department of Computer Science and Engineering, PACE Institute of Technology and Sciences, Ongole, Andhra Pradesh, India Author
Ch Meghana Sri Student, Department of Computer Science and Engineering, PACE Institute of Technology and Sciences, Ongole, Andhra Pradesh, India Author
K Deepthi Student, Department of Computer Science and Engineering, PACE Institute of Technology and Sciences, Ongole, Andhra Pradesh, India Author
M Kaveri Student, Department of Computer Science and Engineering, PACE Institute of Technology and Sciences, Ongole, Andhra Pradesh, India Author
J Aiswarya Student, Department of Computer Science and Engineering, PACE Institute of Technology and Sciences, Ongole, Andhra Pradesh, India Author

DOI:

https://doi.org/10.55524/ijircst.2023.11.3.18

Keywords:

Online Products, Fake Reviews, Identifica-tion, Classification, E-Commerce

Abstract

Today’s modern industry and trade, internet evaluations matter a lot. Buying web items is often influenced by the opinions of other customers. Because of this, unscrupulous folks or organisations attempt to rig customer evaluations to their personal advantage. Using a lodging rating database, this research examines the performance of semi-supervised (SSVD) and supervised (SVD) word extraction methods for detecting false ratings.

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