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Open AccessArticle

Emotion Analysis in Human–Robot Interaction

Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Letná 9, 040 01 Košice, Slovakia
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Electronics 2020, 9(11), 1761; https://doi.org/10.3390/electronics9111761
Received: 25 September 2020 / Revised: 16 October 2020 / Accepted: 20 October 2020 / Published: 23 October 2020
(This article belongs to the Special Issue Human Computer Interaction for Intelligent Systems)
This paper connects two large research areas, namely sentiment analysis and human–robot interaction. Emotion analysis, as a subfield of sentiment analysis, explores text data and, based on the characteristics of the text and generally known emotional models, evaluates what emotion is presented in it. The analysis of emotions in the human–robot interaction aims to evaluate the emotional state of the human being and on this basis to decide how the robot should adapt its behavior to the human being. There are several approaches and algorithms to detect emotions in the text data. We decided to apply a combined method of dictionary approach with machine learning algorithms. As a result of the ambiguity and subjectivity of labeling emotions, it was possible to assign more than one emotion to a sentence; thus, we were dealing with a multi-label problem. Based on the overview of the problem, we performed experiments with the Naive Bayes, Support Vector Machine and Neural Network classifiers. Results obtained from classification were subsequently used in human–robot experiments. Despise the lower accuracy of emotion classification, we proved the importance of expressing emotion gestures based on the words we speak. View Full-Text
Keywords: sentiment analysis; human–robot interaction; dictionary approach; machine learning approach; social robotics sentiment analysis; human–robot interaction; dictionary approach; machine learning approach; social robotics
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Szabóová, M.; Sarnovský, M.; Maslej Krešňáková, V.; Machová, K. Emotion Analysis in Human–Robot Interaction. Electronics 2020, 9, 1761.

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