Emotionally-Aware Chatbots: A Survey
By Endang Wahyu Pamungkas. 2019
45 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Experimental and Cognitive Psychology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Soujanya Poria
- Rada Mihalcea
- Navonil Majumder
- Devamanyu Hazarika
- Erik Cambria
- Sahisnu Mazumder
- Bing Liu
- Louis-philippe Morency
In Experimental and Cognitive Psychology
- Navonil Majumder
- Soujanya Poria
- Rada Mihalcea
- Devamanyu Hazarika
- Bing Liu
- Sahisnu Mazumder
- Erik Cambria
- Alexander Gelbukh
Citations in the corpus
- Lifelong and Continual Learning Dialogue Systems: Learning during Conversation (2021, background): “• Learning emotions and sentiments: Recognizing emotional state (Zhou et al. 2018a; Pamungkas 2019) and sentiments of the user and leverage it to generate empathetic responses can be useful to building therapeutic chatbots.”
- Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, background): “For a brief review of some of the recent works in this area, available corpora and evaluationmetrics, please refer to Pamungkas [162].”