Understanding the performance of AI algorithms in Text-Based Emotion Detection for Conversational Agents

Abstract

Current industry trends demand automation in every aspect, where machines could replace humans. Recent advancements in conversational agents have grabbed a lot of attention from industries, markets, and businesses. Building conversational agents that exhibit human communication characteristics is a need in today's marketplace. Thus, by accumulating emotions, we can build emotionally-aware conversational agents. Emotion detection in text-based dialogues has turned into a pivotal component of conversational agents, enhancing their ability to understand and respond to users' emotional states. This paper extensively compares various AI - techniques adapted to text-based emotion detection for conversational agents. This study covers a wide range of methods ranging from machine learning models to cutting-edge pre-trained models as well as deep learning models. The authors evaluate the performance of these techniques on the benchmark unbalanced topical chat and empathetic dialogue, balanced datasets. This paper offers an overview of the practical implications of emotion detection techniques in conversational systems and their impact on user response. The outcomes of this paper contribute to the ongoing development of empathetic conversational agents, emphasizing natural human-machine interactions.