Detecting Fraud Apps Using Sentimental Analysis

Abstract

Play store offers many applications, but unfortunately, its programs are fraudulent. Such applications damage the phone and can be data theft. Therefore, such programs must be found to identify fraud and theft for users. So, we will make an application that processes the comments, information, and application review. The widespread use of mobile phones and applications throughout the community has helped establish counterfeit applications among today's most prominent online threats. In addition, there are many applications with intentions of fraud available on the internet. Fake behavior is most prevalent in application stores such as the Google play store and the Apple application store. Nowadays, there are so many programs available on the internet because the user cannot always get accurate or genuine reviews about the product on the internet. In this project, we propose the system by developing a program that helps to detect fraudulent programs with comments and data learning analysis. The development of apps was expanded to millions at the play store, making the clients in a fluffy state while downloading the applications. Unfortunately, there are many apps from which any application can misrepresent, so recognition of simple applications is required. Extortion applications essentially manage counterfeit applications. Along these lines, our framework assists the client with recognizing which application is valid. We will propose a method to detect the fraud app based on user reviews and ratings of the application. The reviews will be collected from the play store and categorized into positive or negative result. The user reviews are much important in this system as they may change anytime when a user see suspicious in the application or a there may be any bug in the latest version or update of the application.