This item is non-discoverable
Review Article Emotionally Intelligent Chatbots: A Systematic Literature Review
Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Conversational technologies are transforming the landscape of human-machine interaction. Chatbots are increasingly being used
in several domains to substitute human agents in performing tasks, answering questions, giving advice, and providing social and
emotional support. Therefore, improving user satisfaction with these technologies is imperative for their successful integration.
Researchers are leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques to impart emotional
intelligence capabilities in chatbots. This study provides a systematic review of research on developing emotionally intelligent
chatbots. We employ a systematic approach to gather and analyze 42 articles published in the last decade. The review is aimed
at providing a comprehensive analysis of past research to discover the problems addressed, the techniques used, and the
evaluation measures employed by studies in embedding emotion in chatbot conversations. The study’s findings reveal that
most studies are based on an open-domain generative chatbot architecture. Researchers mainly address the issue of accurately
detecting the user’s emotion and generating emotionally relevant responses. Nearly 57% of the studies use an enhanced
Seq2Seq encoding and decoding of the input of the conversational model. Almost all the studies use both the automatic and
manual evaluation measures to evaluate the chatbots, with the BLEU measure being the most popular method for objective
evaluation.