EmoBot: Artificial emotion generation through an emotional chatbot during general-purpose conversations

IF 2.1 3区心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Cognitive Systems Research Pub Date : 2023-09-09 DOI:10.1016/j.cogsys.2023.101168

Md Ehtesham-Ul-Haque , Jacob D’Rozario , Rudaiba Adnin , Farhan Tanvir Utshaw , Fabiha Tasneem , Israt Jahan Shefa , A.B.M. Alim Al Islam

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引用次数: 0

Abstract

Emotion modeling has always been intriguing to researchers, where detecting emotion is highly focused and generating emotion is much less focused to date. Therefore, in this paper, we aim to exploring emotion generation, particularly for general-purpose conversations. Based on the Cognitive Appraisal Theory and focusing on audio and textual inputs, we propose a novel method to calculate informative variables to evaluate a particular emotion-generating event and six primary emotions. Incorporating such a method of artificial emotion generation, we implement an emotional chatbot, namely EmoBot. Accordingly, EmoBot analyzes continuous audio and textual inputs, calculates the informative variables to evaluate the current situation, generates appropriate emotions, and responds accordingly. An objective evaluation indicates that EmoBot could generate more accurate emotional and semantic responses than a traditional chatbot that does not consider emotion. Additionally, a subjective evaluation of EmoBot demonstrates the appreciation of users for EmoBot over a traditional chatbot that does not consider emotion.

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EmoBot:通过情感聊天机器人在通用对话中产生人工情感

情感建模一直吸引着研究人员，迄今为止，情感检测是高度集中的，而情感产生则不那么集中。因此，在本文中，我们的目标是探索情感的产生，特别是对于通用会话。基于认知评价理论，以音频和文本输入为重点，提出了一种计算信息变量的新方法，以评估特定的情绪产生事件和六种主要情绪。结合这种人工情感生成的方法，我们实现了一个情感聊天机器人，即EmoBot。因此，EmoBot分析连续的音频和文本输入，计算信息变量来评估当前情况，产生适当的情绪，并做出相应的反应。客观评价表明，EmoBot比不考虑情感的传统聊天机器人能够产生更准确的情感和语义反应。此外，对EmoBot的主观评价表明，用户对EmoBot的欣赏程度超过了不考虑情感的传统聊天机器人。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cognitive Systems Research 工程技术-计算机：人工智能

CiteScore

9.40

自引率

5.10%

发文量

审稿时长

>12 weeks

期刊介绍： Cognitive Systems Research is dedicated to the study of human-level cognition. As such, it welcomes papers which advance the understanding, design and applications of cognitive and intelligent systems, both natural and artificial. The journal brings together a broad community studying cognition in its many facets in vivo and in silico, across the developmental spectrum, focusing on individual capacities or on entire architectures. It aims to foster debate and integrate ideas, concepts, constructs, theories, models and techniques from across different disciplines and different perspectives on human-level cognition. The scope of interest includes the study of cognitive capacities and architectures - both brain-inspired and non-brain-inspired - and the application of cognitive systems to real-world problems as far as it offers insights relevant for the understanding of cognition. Cognitive Systems Research therefore welcomes mature and cutting-edge research approaching cognition from a systems-oriented perspective, both theoretical and empirically-informed, in the form of original manuscripts, short communications, opinion articles, systematic reviews, and topical survey articles from the fields of Cognitive Science (including Philosophy of Cognitive Science), Artificial Intelligence/Computer Science, Cognitive Robotics, Developmental Science, Psychology, and Neuroscience and Neuromorphic Engineering. Empirical studies will be considered if they are supplemented by theoretical analyses and contributions to theory development and/or computational modelling studies.