Fraud App Detection Using Sentiment Analysis

Since there are more and more mobile applications used in daily life, it's critical to monitor which ones are secure and which are not. Based just on the reviews listed for each program, one cannot determine how reliable and safe each one is. Therefore, it is essential to verify and start a system to ensure that the apps are authentic or fraudulent. The goal is to create a web system that uses support vector machines and sentiment analysis to identify fraudulent apps before users download them. The purpose of sentiment analysis is to assist in identifying the emotional undertones of words used in online communication. This approach is helpful for keeping an eye on social media and for quickly gauging public sentiment on particular topics. On the internet, the customer may not always find accurate or genuine product reviews. The reviews could be authentic or fraudulent. We can ascertain whether or not the app is authentic by examining evaluations that include remarks from both users and administrators. The system can learn and understand the sentiments and emotions of reviews and other materials by using support vector machines and sentimental analysis. One of the main components of app ranking fraud is the manipulation of reviews. The right app for iOS and Android can be found by examining reviews and comments using emotional analysis and support vector machines. Keywords : Positive negative neutral reviews, Sentiment analysis, Support Vector Machine, Users reviews