Research on emotional interaction and user cognition

Abstract

This paper focuses on the field of emotional interaction, specifically using text data to evaluate expressed emotions. It aims to assess the emotional state of humans in order to determine how robots should adjust their behavior. To detect emotions in text data, various methods and algorithms can be utilized. In this study, the authors combine dictionary methods with machine learning algorithms for emotion detection. However, emotion labels are often vague and subjective, leading to a multi-label problem. To address this, the paper conducts experiments using naive Bayes, support vector machine, and neural network classifiers. Although the accuracy of emotion classification is low, the study demonstrates the importance of specific words in expressing emotions.