Fake review detection using machine learning (Record no. 23177)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250729113256.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250729b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 14141 |
| Author | Gayathri, M. |
| 245 ## - TITLE STATEMENT | |
| Title | Fake review detection using machine learning |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.5(1), May-Aug |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New Delhi |
| Name of publisher, distributor, etc. | Enriched Publications |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 25-30p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Online reviews have become increasingly important in the world of e-commerce, serving as a powerful tool<br/>to establish a business's reputation and attract new customers. However, the rise of fake reviews has<br/>become a growing concern as they can skew the reputation of a business and deceive potential customers.<br/>As a result, detecting fake reviews has become a key area of research in recent years. This project proposes<br/>a machine learning-based approach to detect fake reviews. The method utilizes various feature<br/>engineering techniques to extract different behavioural characteristics of reviewers, such as the length of<br/>reviews and the frequency of review submissions. These characteristics are then used to train different<br/>algorithms, including K-Nearest Neighbors (KNN), Random Forest, and Support Vector Machine (SVM),<br/>to classify reviews as either genuine or fake. The proposed technique was evaluated using a real dataset<br/>extracted from the internet, and the results showed that SVM outperformed the other classifiers in terms of<br/>accuracy. This suggests that SVM is a powerful algorithm for distinguishing between genuine and fake<br/>reviews. However, the study also suggests that there is potential to improve the performance of the model by<br/>integrating more behavioural characteristics of reviewers, such as how frequently they do reviews and how<br/>long it takes them to complete reviews. In conclusion, this project highlights the importance of detecting<br/>fake reviews and proposes a machine learning-based approach to achieve this. The study shows that SVM<br/>is a powerful algorithm for this task, but there is potential for further improvement by incorporating more<br/>reviewer behavioural characteristics. The findings of this research have practical implications for<br/>businesses, consumers, and researchers in the field of e-commerce. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 26865 |
| Co-Author | Siva Teja, Y. S. N. |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | New Delhi Enriched Publications |
| International Standard Serial Number | 2582-7464 |
| Title | Computational intelligence and machine learning |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://www.enrichedpublications.com/ep_admin/jounral/pdf/1741340681.pdf#toolbar=0 |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 29/07/2025 | 2025-1191 | 29/07/2025 | 29/07/2025 | Articles Abstract Database |