Identifying rev model based on online streaming data using deep learning technique
Sr. Software web developer Engineer, Computer science, Germania Insurance, Melissa, Texas.
Research Article
World Journal of Advanced Research and Reviews, 2024, 21(02), 2027-2034
Publication history:
Received on 16 January 2024; revised on 24 February 2024; accepted on 28 February 2024
Abstract:
Web mining has recently grown in popularity as a practical innovation that helps individuals and organizations learn from each other's discoveries. When consumers are in the market for a product, mining helps them narrow their options by focusing on compressed sentiments rather than wasting time on lengthy surveys and drawing out their own plans. Content from surveys is a vital resource for online businesses before customers buy anything. This review covers the history, current state, and potential future of sentiment mining and investigation of review spam. Reader opinion can be swayed by any review, whether it's positive or negative. Consequently, our endeavor employs an automated system for review verification and spam detection. A user can use the proposed DLS-Rev Model to detect and eliminate bogus reviews. The research shows a very trustworthy framework that can detect fake content with 100% accuracy and a 100% extraction rate. The highly pleasing result was obtained by extracting sentiment- and opinion-approved phrases. The project's overarching objective is to make the online review system better for everyone by reducing the negative impact of spammers.
Keywords:
Text Mining; Review; Machine learning; Extraction; Accuracy; Spam
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0