Paper Key : IRJ************264
Author: Abhijit Vasant Kote,Atharva Vijay Otari,Mahavir Devendra Kasar,Shreenath Amol Tambe
Date Published: 21 Oct 2023
Abstract
The proliferation of fraudulent mobile applications has raised serious concerns, necessitating innovative strategies for detection and mitigation. This review paper delves into the promising domain of fraud app detection, with a primary emphasis on leveraging sentiment analysis techniques. Sentiment analysis, a branch of natural language processing, plays a pivotal role in identifying deceptive mobile applications by analysing user reviews, app descriptions, and other textual data for emotional cues and opinions. This review paper comprehensively surveys the existing literature on fraud app detection, offering a detailed account of methodologies, tools, and advancements in the field. It categorizes sentiment analysis-based approaches into supervised, unsupervised, and hybrid models, each with its strengths and limitations. Moreover, it discusses the pivotal role of user reviews, ratings, app descriptions, and developer information as key features in detecting malicious applications. The paper also addresses challenges like fake reviews and biased sentiment expressions in sentiment analysis for fraud app detection. Emphasizing the role of datasets and ethical considerations, this review paper underscores the critical importance of sentiment analysis in enhancing the security of mobile app ecosystems. By providing insights into current techniques, challenges, and future prospects, this paper aims to catalyse the development of more robust fraud app detection systems.