一种用于确定心理和情绪状态的机器学习工具
A Machine Learning Tool to Determine State of Mind and Emotion
摘要：
本文研究了创建一个机器学习工具的可能性，该工具可以通过问卷自动确定个人的心理和情绪状态，而无需人类专家的帮助。在这部著作中，精神和情感的状态被定义为与不基于逻辑或理性的偏好、感觉或观点有关。当一个人给出一个答案时，首先说，“我觉得...... ".该工具旨在模仿心理学家的专业知识，并且在没有任何正式心理学知识的情况下构建。这个想法是通过纯粹的计算方法，通过从用户那里收集的数千个问题来建立专业知识。它的目的是对可能诊断物质成瘾，酗酒，性吸引，艾滋病病毒感染状况，承诺程度，活动倾向等，首先，本文介绍了相关文献，并根据数据收集方法进行分类。另一种分类是根据偏好、情感、分组和规则创建的，以实现对心理和情感状态的更深层次的解释和更好的理解。第二，建议的工具是使用网络成瘾问卷10个问题和292名受访者。此外，通过建立的机器学习模型，对成瘾维度进行了初步探讨。机器学习方法，即人工神经网络（ANN）和支持向量机（SVM），用于确定应答者的状态的真或假或程度。
英文摘要：
This paper investigates the possibility of creating a machine learning tool that automatically determines the state of mind and emotion of an individual through a questionnaire, without the aid of a human expert. The state of mind and emotion is defined in this work as pertaining to preference, feelings, or opinion that is not based on logic or reason. It is the case when a person gives out an answer to start by saying, "I feel...". The tool is designed to mimic the expertise of a psychologist and is built without any formal knowledge of psychology. The idea is to build the expertise by purely computational methods through thousands of questions collected from users. It is aimed towards possibly diagnosing substance addiction, alcoholism, sexual attraction, HIV status, degree of commitment, activity inclination, etc. First, the paper presents the related literature and classifies them according to data gathering methods. Another classification is created according to preference, emotion, grouping, and rules to achieve a deeper interpretation and better understanding of the state of mind and emotion. Second, the proposed tool is developed using an online addiction questionnaire with 10 questions and 292 respondents. In addition, an initial investigation on the dimension of addiction is presented through the built machine learning model. Machine learning methods, namely, artificial neural network (ANN) and support vector machine (SVM), are used to determine a true or false or degree of state of a respondent.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
