Human relationships are influenced by the underlying emotions in their interactions. With the increasing use of social networks, relationships from textual data can also be inferred from online interactions. Such interactions result in massive amount of textual data which is available in the form of text messages, emails, and social media posts. Identification and analysis of human relationships are useful for numerous applications ranging from cybersecurity to public health. In this paper, we present a method called RIEA (Relationship Identification using Emotion Analysis), for identifying relationships between multiple intelligent agents by analyzing the conversation between them. The objective of our work is to combine concepts of cognitive psychology and natural language processing (NLP) to extract emotions and map them onto a set of relationships and analyze how relationships transform over time. We employ psychological models to label a large corpus of conversations and apply machine learning techniques to determine emotion-to-relationship mapping. We use four distinct association classes and four attachment styles using best-worst scaling method for classification. Combining the attachment and association styles given in research literature gives us the relationship combinations for our analysis. Additionally, this work studies the most common changes of behaviors and emotions and the corresponding transformations in human relationships. Our results show that RIEA can correctly detect interpersonal relationships with an accuracy of 85%. The evaluation shows that RIEA can accurately identify interpersonal relationships from conversations and can be extended for identifying more complex relationships. This study also highlights the effect of changes in emotional behavior in the development of relationships over time.

人际关系受互动中潜在情感的影响。随着社交网络的使用越来越多，文本数据的关系也可以从在线交互中推断出来。这样的交互导致大量文本数据，这些文本数据以文本消息，电子邮件和社交媒体帖子的形式可用。人际关系的识别和分析对于从网络安全到公共卫生的众多应用非常有用。在本文中，我们提出了一种称为RIEA（使用情感分析的关系识别）的方法，该方法用于通过分析多个智能代理之间的对话来识别它们之间的关系。我们的工作目标是将认知心理学和自然语言处理（NLP）的概念相结合，以提取情感并将其映射到一组关系上，并分析关系如何随时间而变化。我们采用心理模型来标记大量会话，并应用机器学习技术来确定情感与关系的映射。我们使用最差缩放比例方法对四个不同的关联类和四个附件样式进行分类。结合研究文献中给出的依恋和联想风格，可以为我们的分析提供关系组合。此外，这项工作研究了行为和情感的最常见变化以及人际关系的相应转变。我们的结果表明，RIEA可以正确检测人际关系，准确率达85％。评估表明，RIEA可以从对话中准确地识别人际关系，并且可以扩展用于识别更复杂的关系。这项研究还强调了情感行为的变化对人际关系发展的影响。