Analyzing the Efficacy of Adversarial Learning Techniques

in Improving Sentiment Analysis for Socially Implemented IoMT Systems

Abstract

The integration of medical devices and applications with internet-connected technologies,
known as Internet of Medical Things (IoMT), has enabled remote monitoring and management of patient
health. In socially implemented IoMT systems, such as mobile health applications or wearable devices,
analyzing the sentiment expressed in patient feedback or reviews is crucial to ensure patient satisfaction
and improve the quality of care. However, such sentiment analysis can be easily manipulated by
intentional attacks or adversarial inputs, such as fake reviews or manipulated feedback. This paper
presents a review of the efficacy of adversarial learning-based sentiment analysis techniques in improving
sentiment analysis (SA) in socially implemented IoMT systems. This paper discusses the challenges of
sentiment analysis in IoMT systems and how adversarial learning can be applied to improve the
robustness of sentiment analysis models. This study highlights the potential of adversarial learning
techniques to improve the accuracy and effectiveness of sentiment analysis in socially implemented IoMT
systems. However, the paper also shows that more research is needed to fully understand the impact of
adversarial inputs on sentiment analysis models and to develop more sophisticated and robust models.
This paper concludes by discussing future directions for research in this domain, including the need for
better data privacy and security measures to prevent adversarial attacks in IoMT systems.