E3VA: Enhancing Emotional Expressiveness in Virtual Conversational Agents

2026-02-25

AI Summary

This study addresses a critical limitation of current virtual conversational agents—namely, their rich knowledge base coupled with a notable deficiency in emotional perception and expression, which hinders their ability to adapt to users’ affective states and ultimately constrains interaction quality and user engagement. To overcome this, the work proposes a novel approach that explicitly integrates emotional context into general-purpose dialogue generation by synergistically combining sentiment analysis, natural language processing, and generative AI. The resulting emotion-aware conversational agent not only accurately recognizes user emotions but also produces empathetic and expressive responses. Preliminary empirical evaluations demonstrate that this method significantly enhances user engagement, system usability, and overall dialogue quality compared to conventional content-focused systems.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Humans and AI: Emotional Intelligence
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

With the advent of generative AI and large language models, embodied conversational agents are becoming synonymous with online interactions. These agents possess vast amounts of knowledge but suffer from exhibiting limited emotional expressiveness. Without adequate expressions, agents might fail to adapt to users' emotions, which may result in a sub-optimal user experience and engagement. Most current systems prioritize content based responses, neglecting the emotional context of conversations. Research in this space is currently limited to specific contexts, like mental health. To bridge this gap, our project proposes the implementation of expressive features in a virtual conversational agent which will utilize sentiment analysis and natural language processing to inform the generation of empathetic, expressive responses. The project delivers a functional conversational agent capable of assessing and responding to user emotions accordingly. We posit this will enhance usability, engagement, and the overall quality of conversations and present results from an exploratory pilot study investigating the same.

Problem

• Emotional Expressiveness
• Virtual Conversational Agents
• User Engagement
• Emotion Adaptation
• Conversational AI

Innovation

• Emotional Expressiveness
• Virtual Conversational Agents
• Sentiment Analysis
• Empathetic Response Generation
• Natural Language Processing