# Exploring Emotion-Sensitive LLM-Based Conversational AI

arXiv: https://arxiv.org/abs/2502.08920 · HTML: https://mlsanity.com/paper/2502.08920/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Towards an Online Empathetic Chatbot with Emotion Causes](https://mlsanity.com/paper/2105.11903/) (2021)
- [Emotionally-Aware Chatbots: A Survey](https://mlsanity.com/paper/1906.09774/) (2019)

## Built on by (most cited first, 4 of 4)

- [Project Riley: Multimodal Multi-Agent LLM Collaboration with Emotional Reasoning and Voting](https://mlsanity.com/paper/2505.20521/) (2025)
- [Do We Talk to Robots Like Therapists, and Do They Respond Accordingly? Language Alignment in AI Emotional Support](https://mlsanity.com/paper/2506.16473/) (2025)
- [Benchmarking and Learning Real-World Customer Service Dialogue](https://mlsanity.com/paper/2510.22143/) (2025)
- [Using AI in engineering education: a balancing act, driven by clear purpose](https://mlsanity.com/paper/2606.16626/) (2026)

## Abstract (arXiv)

Conversational AI chatbots have become increasingly common within the customer service industry. Despite improvements in their emotional development, they often lack the authenticity of real customer service interactions or the competence of service providers. By comparing emotion-sensitive and emotion-insensitive LLM-based chatbots across 30 participants, we aim to explore how emotional sensitivity in chatbots influences perceived competence and overall customer satisfaction in service interactions. Additionally, we employ sentiment analysis techniques to analyze and interpret the emotional content of user inputs. We highlight that perceptions of chatbot trustworthiness and competence were higher in the case of the emotion-sensitive chatbot, even if issue resolution rates were not affected. We discuss implications of improved user satisfaction from emotion-sensitive chatbots and potential applications in support services.