Emotion Analysis in Human–Robot Interaction

Abstract

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.

Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models

“…The neutral robot performed the worst. Similar findings were found by Szabóová [25], who reported that their robot with emotion expression, for example, voice pitch, was better rated overall. Participants even thought that the robot appeared capable of understanding, compared to the neutral robot.…”

“…Sentiment analysis is a sophisticated interdisciplinary field that connects linguistic and text mining with AI, predominantly for NLP [25]. Dominant sentiment analysis strategies may include emotion detection, subjectivity detection, or polarity classification (e.g., classifying positive, negative and neutral sentiments [26]).…”

“…These techniques require an understanding of complicated human psychology. For instance, they may include instant and short-lasting emotions [27], longer-lasting and unintentional moods [28], and feelings or responses to emotions [25].…”

“…The importance of personalisation in HRI is widely reported, and some experiments have been conducted to compare robotic systems with and without sentiment abilities, including expressing and analysing sentiment [25,[29][30][31][32]. These studies used the NAO, Kismet, and Reeti robots.…”

Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach

“…When using the method of sentiment analysis, many researchers used different analysis methods. Zhang et al [ 56 ], Shakil et al [ 57 ] and Phu et al [ 58 ] used dictionary-based methods; Truică et al [ 59 ], Messina et al [ 60 ], Wang et al [ 61 ] and other studies used machine learning, whereas Szabóová et al [ 62 ], Ahmed et al [ 63 ] combine the dictionary-based methods and machine learning. The sentiment dictionary-based method is good at processing fine-grained text sentiment analysis, which is conducive to analyzing sentiment characteristics in specific fields [ 64 ].…”