• Poossan Gopikrishnan
• Ganesh Rahangdale
• Joel Craig
• Shivam Butle
• Shoyeb Shiekh

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.

Fraud Apps Detection,Sentiment Analysis, Technological development.

"Fraud App Detection Using Sentimental Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.o196-o199, April-2024, Available :http://www.jetir.org/papers/JETIR2404F27.pdf

"Fraud App Detection Using Sentimental Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppo196-o199, April-2024, Available at : http://www.jetir.org/papers/JETIR2404F27.pdf