# Emotionally-Aware Chatbots: A Survey

arXiv: https://arxiv.org/abs/1906.09774 · HTML: https://mlsanity.com/paper/1906.09774/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 8 of 8)

- [Empathetic Conversational Systems: A Review of Current Advances, Gaps, and Opportunities](https://mlsanity.com/paper/2206.05017/) (2022)
- [A Survey of Personality, Persona, and Profile in Conversational Agents and Chatbots](https://mlsanity.com/paper/2401.00609/) (2023)
- [Exploring Emotion-Sensitive LLM-Based Conversational AI](https://mlsanity.com/paper/2502.08920/) (2025)
- [ChatGPT on the Road: Leveraging Large Language Model-Powered In-vehicle Conversational Agents for Safer and More Enjoyable Driving Experience](https://mlsanity.com/paper/2508.08101/) (2025)
- [Towards Multi-Turn Empathetic Dialogs with Positive Emotion Elicitation](https://mlsanity.com/paper/2204.10509/) (2022)
- [Do You Know My Emotion? Emotion-Aware Strategy Recognition towards a Persuasive Dialogue System](https://mlsanity.com/paper/2206.12101/) (2022)
- [LastResort at SemEval-2024 Task 3: Exploring Multimodal Emotion Cause Pair Extraction as Sequence Labelling Task](https://mlsanity.com/paper/2404.02088/) (2024)
- [Computational Charisma -- A Brick by Brick Blueprint for Building Charismatic Artificial Intelligence](https://mlsanity.com/paper/2301.00142/) (2022)

## Abstract (arXiv)

Textual conversational agent or chatbots' development gather tremendous traction from both academia and industries in recent years. Nowadays, chatbots are widely used as an agent to communicate with a human in some services such as booking assistant, customer service, and also a personal partner. The biggest challenge in building chatbot is to build a humanizing machine to improve user engagement. Some studies show that emotion is an important aspect to humanize machine, including chatbot. In this paper, we will provide a systematic review of approaches in building an emotionally-aware chatbot (EAC). As far as our knowledge, there is still no work focusing on this area. We propose three research question regarding EAC studies. We start with the history and evolution of EAC, then several approaches to build EAC by previous studies, and some available resources in building EAC. Based on our investigation, we found that in the early development, EAC exploits a simple rule-based approach while now most of EAC use neural-based approach. We also notice that most of EAC contain emotion classifier in their architecture, which utilize several available affective resources. We also predict that the development of EAC will continue to gain more and more attention from scholars, noted by some recent studies propose new datasets for building EAC in various languages.