CS619 Detecting Fraud Apps using Sentiment Analysis | CS619 Prototype

Welcome to the prototype demonstration of my BSCS Final Year Project (CS619). In this phase, I have implemented the core functionality of the system to identify potentially fraudulent mobile applications based on user reviews and sentiment patterns.

📋 Project Overview
The primary goal of this project is to create a security layer for users by analyzing the "language of fraud" in app store reviews. Instead of just looking at star ratings, our system uses Natural Language Processing (NLP) to detect deceptive behavior, scam reports, and bot-generated reviews.

⚙️ Features Included in this Prototype:
User Interface (UI): A clean dashboard built with [Mention your Tech, e.g., Python Flask/Django].

Data Input: Ability to take app reviews as input for analysis.
Sentiment Engine: Basic implementation of sentiment scoring (Positive, Neutral, Negative).

Fraud Logic: Identifying "High Risk" keywords like 'scam', 'fake', 'stole', and 'refund'.

🛠️ Tools & Technologies:
Language: Python
Libraries: NLTK
Frontend: HTML/CSS/
Database: MySQL

For Paid Projects:
https://wa.me/qr/NC77LIRSUCEQF1

🎓 For VU Students:
This video covers the Prototype Phase requirements, including the execution of the main module and basic GUI as per the CS619 guidelines.
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