EMOTION RECOGNITION USING LARGE LANGUAGE MODEL

Abstract

Emotion recognition is the process of recognizing human emotions from facial expressions, voice patterns, or text, utilizing various techniques like Machine Learning or Natural Language Processing. It is a crucial aspect of human interaction that has drawn a significant amount of attention, particularly with the emergence of natural language processing (NLP) techniques. This paper explores the use of transformer based large language models in the field of emotion recognition due to their ability to capture complex contextual relationships in data. Large Language Models are an important development in the field of emotion recognition as they can accurately analyze and interpret complex human emotions from textual data. This innovation has enhanced the context-sensitivity of emotional analysis, opening new possibilities in various fields like mental health care, and customer services, where understanding complex emotional states is crucial. This research provides an overview of the current development and approaches of emotion recognition using Large Language Models (LLMs). It explores the recent methodologies in LLM-based emotion recognition, emphasizing the models' unparalleled capabilities to understand and interpret the complex emotions in text. It explores how well transformer architectures adeptly capture and model the nuances of emotion in textual data. Through extensive experimentation and comparative analysis, this research evaluates various model's performance in precisely identifying and classifying emotions. The research further delves into the challenges and limitations faced by current LLMs in emotion recognition. Additionally, addresses the research gaps and outlines possible future paths. The outcome of this work improves the understanding of LLM based techniques for emotion recognition and provides important new information about their usefulness in various real-world contexts.