A Review on Virtual Assistant Using NLP

The emergence and adoption of Natural Language Processing (NLP) powered Virtual Assistants (VAs) have been unprecedented in recent years and have become an essential part of seamless human-computer interaction. This phenomenon has been driven and witnessed speech recognition, intent understanding, dialog management, and even the generative models-defining the scope of applications within the last 10 years. This paper aims to provide an overview of the available literature on NLP-based Virtual Assistants within the scope of the years 2015 to 2025. This paper reviews and analyzes 65 peer-reviewed publications within the framework of a selection and review methodology from the databases of IEEE Xplore, SpringerLink, ScienceDirect, ACM Digital Library and Scopus. The paper examines various fields of study, such as the Technology and Analytics of Virtual Assistants, Implementation, Datasets and Metrics in the fields of Technology and Analytics of Virtual Assistants in Robotics, Healthcare, Education, Customer Service and Personal Productivity.The results show significant advancements in contextual comprehension, response accuracy, and user adaptability that have been made possible by deep learning architectures, transformer-based models, and big language models. irrespective of these developments, a number of issues still exist, such as restricted customisation, inadequate support for low-resource languages, poor multimodal integration, a heavy reliance on cloud infrastructure, and moral dilemmas pertaining to safety, privacy, and transparency. In order to create adaptive, privacy-preserving, emotionaware, and domain-specific virtual assistants that can function effectively in real-world settings, future research directions are identified in the review's conclusion. The information presented is intended to help academics and developers create the next generation of intelligent, inclusive, and morally sound assistance systems.