Recent Trends in AI-Driven Sentiment Analysis, Healthcare Applications, and Wireless Communication Networks

Authors: Samya Das¹, Dr. Sanchita Saha², Dr. Ankan Bhattacharya³, Achyut Mitra⁴, Asit Kumar Nayek⁵

1Computer Science and Engineering, Jadavpur University, Kolkata, India.
2Computer Science and Engineering, Techno International New Town, Kolkata, India.
3Electronics and Communication Engineering, Hooghly Engineering & Technology College, Kolkata, India.
4Computer Science and Engineering, JIS University, Kolkata, India.
5Computer Science and Engineering, Haldia Institute of Technology, Kolkata, India.

samyadas2107@gmail.com, sanchita.cse2007@gmail.com, ankan@hetc.ac.in, achyutmitra683@gmail.com, asit.nayek@gmail.com

Published in: Journal of Computer Science and Engineering in Innovations and Research (ISSN No: 3049-1762 online)

Publication Date: June 15, 2026

Pages: 1–4

📄 Abstract

Sentiment analysis provides healthcare providers the ability to understand patient emotions, feedback, and behavioral patterns from textual and speech data. Simultaneously, wireless communication networks such as 5G, 6G, Wireless Sensor Networks (WSNs), Internet of Medical Things (IoMT), and Wireless Body Area Networks (WBANs) facilitate real-time healthcare monitoring and remote medical services. This paper reviews recent trends, advancements, challenges, and future directions in AI-driven sentiment analysis integrated with healthcare applications and wireless communication systems. The study highlights emerging technologies including federated learning, explainable AI, edge intelligence, and emotional AI for next-generation smart healthcare systems.

🔑 Keywords

Artificial Intelligence, Sentiment Analysis, Healthcare Applications, Wireless Networks, IoMT, Deep Learning, 5G/6G, Explainable AI

I. Introduction

Healthcare systems have underwent a revolution thanks to the quick training of artificial intelligence (AI), which has made intelligent diagnosis, patient monitoring, emotion detection, and individualized treatment promising [1,2,8]. Understanding patient thoughts, feelings, and healthcare experiences through textual and voice-based interactions is made possible by sentiment analysis, a subfield of natural language processing (NLP) [3,4,9,23].

In order to enable real-time medical data transfer, modern healthcare infrastructures depend more and more on wireless communication technologies including Wireless Sensor Networks (WSNs), the Internet of Medical Things (IoMT), and fifth/sixth-generation (5G/6G) networks [16,17]. Improved patient care, remote monitoring, lower latency, and intelligent decision-making are all made possible by the combination of AI-driven analytics with wireless healthcare systems [7,8,14]. A thorough examination of current developments in wireless communication networks, healthcare applications, and AI-driven sentiment analysis is presented in this study.