Fraud App Detection using Sentimental Analysis
Published: 2023
Author(s) Name: Ganesh Rahangdale, Joel Craig, Poossan Gopikrishnan, Shivam Butle, Shoyeb Sheikh and Priya Narnavere |
Author(s) Affiliation: J D College of Engineering and Management, Fetri Nagpur, Maharashtra, India.
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Abstract
This project aims to develop a fraud app detection system using sentiment analysis. The system leverages Java 1.8 Spring Boot, React, HTML, CSS, JavaScript, and Bootstrap to create a robust web application. The methodology involves collecting user reviews and comments, preprocessing the data, and applying sentiment analysis models to determine sentiment scores. The system then uses predefined fraud criteria to flag potentially fraudulent reviews. Integrating the system into a Java Spring Boot backend and visualizing results using React provides real-time monitoring and investigation. Continuous improvement, user feedback handling, and effective model selection ensure enhanced accuracy and adaptability to evolving fraudulent patterns. This project presents an integrated fraud detection system for user reviews, utilizing sentiment analysis within a Java 1.8 Spring Boot backend and React frontend. It encompasses data collection, preprocessing, sentiment analysis, and predefined fraud criteria to flag suspicious reviews. Real-time monitoring and investigation capabilities are offered through an intuitive web interface. The project’s commitment to continuous improvement, user feedback integration, and effective model selection ensures adaptability to evolving fraudulent patterns, enhancing accuracy and preserving the credibility of online platforms in an era where user-generated content profoundly influences consumer decisions.
Keywords: Fraud apps detection, Sentiment analysis, Technological development.
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