Machová Kristína, Szabóova Martina, Paralič Ján, Mičko Ján
Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Košice, Slovakia.
Department of Social Sciences, Technical University of Košice, Košice, Slovakia.
Front Psychol. 2023 Sep 20;14:1190326. doi: 10.3389/fpsyg.2023.1190326. eCollection 2023.
Emotions are an integral part of human life. We know many different definitions of emotions. They are most often defined as a complex pattern of reactions, and they could be confused with feelings or moods. They are the way in which individuals cope with matters or situations that they find personally significant. Emotion can also be characterized as a conscious mental reaction (such as anger or fear) subjectively experienced as a strong feeling, usually directed at a specific object. Emotions can be communicated in different ways. Understanding the emotions conveyed in a text or speech of a human by a machine is one of the challenges in the field of human-machine interaction. The article proposes the artificial intelligence approach to automatically detect human emotions, enabling a machine (i.e., a chatbot) to accurately assess emotional state of a human and to adapt its communication accordingly. A complete automation of this process is still a problem. This gap can be filled with machine learning approaches based on automatic learning from experiences represented by the text data from conversations. We conducted experiments with a lexicon-based approach and classic methods of machine learning, appropriate for text processing, such as Naïve Bayes (NB), support vector machine (SVM) and with deep learning using neural networks (NN) to develop a model for detecting emotions in a text. We have compared these models' effectiveness. The NN detection model performed particularly well in a multi-classification task involving six emotions from the text data. It achieved an F1-score = 0.95 for sadness, among other high scores for other emotions. We also verified the best model in use through a web application and in a Chatbot communication with a human. We created a web application based on our detection model that can analyze a text input by web user and detect emotions expressed in a text of a post or a comment. The model for emotions detection was used also to improve the communication of the Chatbot with a human since the Chatbot has the information about emotional state of a human during communication. Our research demonstrates the potential of machine learning approaches to detect emotions from a text and improve human-machine interaction. However, it is important to note that full automation of an emotion detection is still an open research question, and further work is needed to improve the accuracy and robustness of this system. The paper also offers the description of new aspects of automated detection of emotions from philosophy-psychological point of view.
情感是人类生活中不可或缺的一部分。我们知晓许多关于情感的不同定义。情感最常被定义为一种复杂的反应模式，它们可能会与感觉或情绪相混淆。情感是个体应对他们认为具有个人重要性的事情或情况的方式。情感也可被描述为一种有意识的心理反应（如愤怒或恐惧），主观上体验为一种强烈的感觉，通常指向一个特定的对象。情感可以通过不同的方式进行传达。让机器理解人类文本或言语中传达的情感是人机交互领域的挑战之一。本文提出了一种人工智能方法来自动检测人类情感，使机器（即聊天机器人）能够准确评估人类的情绪状态并相应地调整其交流方式。这一过程的完全自动化仍然是一个问题。基于从对话文本数据所代表的经验中进行自动学习的机器学习方法可以填补这一空白。我们使用基于词汇表的方法以及适用于文本处理的经典机器学习方法（如朴素贝叶斯（NB）、支持向量机（SVM）），并利用神经网络（NN）进行深度学习，来开发一个用于检测文本中情感的模型。我们比较了这些模型的有效性。在涉及文本数据中的六种情感的多分类任务中，神经网络检测模型表现尤为出色。对于悲伤情绪，它的F1分数 = 0.95，对于其他情感也有较高的分数。我们还通过一个网络应用程序以及在聊天机器人与人类的交流中验证了最佳使用模型。我们基于检测模型创建了一个网络应用程序，它可以分析网络用户输入的文本，并检测帖子或评论文本中表达的情感。由于聊天机器人在交流过程中拥有关于人类情绪状态的信息，因此情感检测模型也被用于改善聊天机器人与人类的交流。我们的研究展示了机器学习方法从文本中检测情感并改善人机交互的潜力。然而，需要注意的是，情感检测的完全自动化仍然是一个开放的研究问题，还需要进一步开展工作来提高该系统的准确性和鲁棒性。本文还从哲学 - 心理学角度对情感自动检测的新方面进行了描述。
