Published Paper Details:

The problem of fraudulent mobile applications has grown significantly in importance as a result of the quick development of mobile technology and the rising popularity of mobile applications. These malicious apps not only endanger user's devices but also steal personal information. To safeguard users from potential harm, it is crucial to track down and identify fraudulent mobile applications. With the help of sentiment analysis and the Naive Bayes classifier, SVM etc.., this project is developed for identifying fraudulent applications based on user reviews. The goal is to create a framework that uses data mining and sentiment analysis to analyse user reviews and find review-based evidence of fraud. This projectseek to evaluate the authenticity and dependability of mobile applications before users download them by utilizing sentiment analysis. The suggested method involves gathering user reviews from the Google Play store and classifying them as positive or negative using sentiment analysis. Based on the opinions expressed in the reviews, the Naive Bayes classifier, SVM etc.., is used to categorize applications as either legitimate or fraudulent. By giving users a tool to make educated decisions about the applications they download, this strategy empowers users. Users will be able to recognize fraudulent applications and steer clear of any risks involved with downloading them by putting this framework into place. While giving users a trustworthy way to distinguish between fraudulent and legitimate applications, the system will help to ensure the security and integrity of the mobile application market.