Fraud App Detection using Sentiment Analysis

ABSTRACT for Fraud App Detection using Sentiment Analysis:

The tremendous increase in mobile phone users, also make the increase in the usage of mobile apps. Nowadays users prefer to go for an mobile app instead of a website. The objective is to develop a system in detecting fraud apps before the user downloads by using sentimental analysis and data mining.

Sentimental analysis is to help in determining the emotional tones behind words which are expressed in online. This method is useful in monitoring social media and helps to get a brief idea of the public’s opinion on certain issues. The user cannot always get correct or true reviews about the product on the internet.

We can check for user’s sentimental comments on multiple application. The reviews may be fake or genuine. Analyzing the rating and reviews together involving both user and admins comments, we can determine whether the app is genuine or not.

Using sentimental analysis and Machine Learning, the machine is able to learn and analyze the sentiments, emotions about reviews and other texts. The manipulation of review is one of the key aspects of App ranking fraud. We have used LSTM model to predict the results.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED for Fraud App Detection using Sentiment Analysis:

LSTM Algorithm

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 4 GB.

SOFTWARE REQUIREMENTS:

Operating system : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

Python Projects

Fraud App Detection using Sentiment Analysis