• Prof.Swati Khokale
• Rutuja Kakuste
• Priyanka Thombare
• Rutvik Patil
• Shubham Pawar

Analysis With the widespread use of mobile applications in everyday life, it is critical to keep track of which ones are safe and which are not. The reviews that are mentioned for each application cannot be used to determine how safe and true the application is. As a result, it is necessary to keep track of and develop a system to ensure that the apps available are genuine or not. The goal is to create a system that detects fraudulent apps before the user downloads them using sentimental analysis and data mining. Sentimental analysis is used to help determine the emotional undertones of words expressed online. This method is useful for monitoring social media and getting a quick sense of the public's opinion on specific issues. on the internet, the user may not always find accurate or true product reviews. We can search for sentimental comments from users across multiple applications. The reviews could be fake or real. We can determine whether the app is genuine or not by analyzing the rating and reviews together, which include both user and admin comments. The machine can learn and analyze sentiments, emotions, and other texts using sentimental analysis and data mining. One of the most important aspects of App ranking fraud is the manipulation of reviews. Analyzing reviews and comments using sentimental analysis and data mining might help in finding the best app for both the Android and iOS platforms.

Sentiment analysis, fraud app detection, Machine Learning, data mining

"Detection of Fraud Applications using Sentiment Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.d364-d367, May-2022, Available :http://www.jetir.org/papers/JETIR2205466.pdf

"Detection of Fraud Applications using Sentiment Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppd364-d367, May-2022, Available at : http://www.jetir.org/papers/JETIR2205466.pdf