FRAUD APP DETECTION USING SENTIMENT ANALYSIS

FRAUD APP DETECTION USING SENTIMENT ANALYSIS

1 YASHODHA P G,2 SACHIN S M

[1]Assistant  Professor. Department of MCA, BIET, Davangere

[2]Student, Department of Master of Computer Applications, BIET, Davangere

Abstract

The swift expansion of smartphone apps has resulted in a surge in fraudulent programs, endangering users and the reliability of app stores. Conventional techniques for app appraisal frequently fall short in distinguishing between real and fake applications. This study presents a thorough method for spotting phony mobile apps by employing sentiment analysis and data mining. The system examines user evaluations and comments and uses LSTM (Long Short-Term Memory) models to discern between genuine and fraudulent opinions. Sentiment analysis integration improves the identification of questionable app activity by offering greater insights into user comments. By reducing the quantity of fraudulent programs, improving app store integrity, and giving users accurate results, this strategy seeks to create a mobile app ecosystem that is more dependable and safe.

Keywords: Fraudulent mobile applications, app store integrity, data mining, sentiment analysis, Long Short-Term Memory (LSTM) models.