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 article extensively compares various artificial intelligence techniques adapted to text-based emotion detection for conversational agents. The study covers a wide range of methods, from machine learning models to cutting-edge pre-trained models and deep learning models. We evaluate the performance of these techniques on the benchmark unbalanced Topical-Chat and balanced Empathetic Dialogue datasets. This article offers an overview of the practical implications of emotion detection techniques in conversational systems and their impact on user response. The outcomes of this work contribute to the ongoing development of empathetic conversational agents, emphasizing natural human-machine interactions.