Fake Review Detection Techniques on Online Products: An Empirical Study
Satya Prakash Choukse1 , Pooja Meena2 , Chetan Agrawal3
- Dept. of CSE, Research Scholar, RITS, Bhopal, India.
- Dept. of CSE, Assistant Professor, RITS, Bhopal, India.
- Dept. of CSE, Assistant Professor, RITS, Bhopal, India.
Section:Research Paper, Product Type: Journal Paper
Volume-12 ,
Issue-11 , Page no. 14-20, Nov-2024
CrossRef-DOI: https://doi.org/10.26438/ijcse/v12i11.1420
Online published on Nov 30, 2024
Copyright © Satya Prakash Choukse, Pooja Meena, Chetan Agrawal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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How to Cite this Paper
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IEEE Style Citation: Satya Prakash Choukse, Pooja Meena, Chetan Agrawal, “Fake Review Detection Techniques on Online Products: An Empirical Study,” International Journal of Computer Sciences and Engineering, Vol.12, Issue.11, pp.14-20, 2024.
MLA Style Citation: Satya Prakash Choukse, Pooja Meena, Chetan Agrawal "Fake Review Detection Techniques on Online Products: An Empirical Study." International Journal of Computer Sciences and Engineering 12.11 (2024): 14-20.
APA Style Citation: Satya Prakash Choukse, Pooja Meena, Chetan Agrawal, (2024). Fake Review Detection Techniques on Online Products: An Empirical Study. International Journal of Computer Sciences and Engineering, 12(11), 14-20.
BibTex Style Citation:
@article{Choukse_2024,
author = {Satya Prakash Choukse, Pooja Meena, Chetan Agrawal},
title = {Fake Review Detection Techniques on Online Products: An Empirical Study},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2024},
volume = {12},
Issue = {11},
month = {11},
year = {2024},
issn = {2347-2693},
pages = {14-20},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5733},
doi = {https://doi.org/10.26438/ijcse/v12i11.1420}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v12i11.1420}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5733
TI - Fake Review Detection Techniques on Online Products: An Empirical Study
T2 - International Journal of Computer Sciences and Engineering
AU - Satya Prakash Choukse, Pooja Meena, Chetan Agrawal
PY - 2024
DA - 2024/11/30
PB - IJCSE, Indore, INDIA
SP - 14-20
IS - 11
VL - 12
SN - 2347-2693
ER -
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Abstract
E-commerce websites are becoming an essential component of daily living. The epidemic has quickly moved into the digital era and changed people`s online buying habits. Many individuals research items before making an online purchase by reading product reviews. Thus, reviews are crucial to a customer`s decision to purchase any goods. As a result, social media platforms and e-commerce sites like Flipkart, Amazon, and others get more bogus reviews. Robust and dependable approaches are required to identify phony reviews, which may be advantageous to both the seller and the client. Phony reviews have the power to promote poor items and disparage good ones. The goal of this survey article is to provide a broad overview of the many strategies used to these sorts of problems. This paper provides a thorough examination of several techniques to spam detection in machine learning (ML), natural language processing (NLP), sentiment analysis, and deep learning (DL).
Key-Words / Index Term
Fake Reviews, Machine Learning, Sentimental Analysis, Deep Learning, Natural Language Processing
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