We introduce Target-Event-Agent Networks (TEA Nets) as a computational framework to extract subjects (``Agents"), verbs (``Events"), and objects (``Targets") from texts. Grounded in cognitive network science and artificial intelligence, TEA Nets are implemented as an open-source Python library. We test TEA Nets in three case studies, demonstrating the framework's ability to perform interpretable emotion detection, semantic frame analyses, and linguistic inquiries across conspiracy texts and textual responses generated by LLMs. In the LOCO conspiracy corpus, TEA Nets revealed that highly conspiratorial narratives (4,227 texts) linked personal pronouns (``I", ``you", ``we") with the same actions twice as frequently as low-similarity conspiracy narratives. High-conspiracy narratives connected person-focused elements (``you", ``people") through actions eliciting anger above the random baseline ($z = 2.63, p < .05$), a trend absent in low-similarity conspiracy narratives, which emphasized scientific actors (``researcher", ``scientist"). In the HOPE and CounseLLMe datasets of 212 (human) and 200 (LLM-based) psychotherapy transcripts, respectively, TEA Nets highlighted emotional differences. When expressing feelings, Claude 3 Haiku, GPT-3.5, and humans used sad words with higher frequency than random expectations but Haiku expressed sadness with lower emotional intensity than humans ($U = 1243.5, p = .036$). We discuss these differences in the context of psychotherapy training on LLM-simulated patients. Our results show that Target-Event-Agent Networks can extract relevant emotional, syntactic, and semantic insights from narratives, opening new avenues for text analysis with cognitive network science.

翻译：我们提出目标-事件-行动者网络（简称TEA Nets）作为从文本中提取主体（“行动者”）、动词（“事件”）和客体（“目标”）的计算框架。TEA Nets根植于认知网络科学与人工智能，以开源Python库形式实现。我们通过三项案例研究测试TEA Nets，展示了该框架在阴谋论文本及大语言模型生成文本中进行可解释情感检测、语义框架分析和语言学探究的能力。在LOCO阴谋论语料库中，TEA Nets揭示：高度阴谋叙事文本（4,227篇）将人称代词（“我”“你”“我们”）与相同动作关联的频率是低相似度阴谋叙事文本的两倍；高度阴谋叙事文本通过引发愤怒情绪的动作连接以人为中心的元素（“你”“人们”），其关联强度高于随机基线（z = 2.63，p < .05），而低相似度阴谋叙事文本中缺乏此趋势，后者更强调科学行动者（“研究者”“科学家”）。在分别包含212份（人类）和200份（基于大语言模型的）心理治疗对话记录的HOPE与CounseLLMe数据集中，TEA Nets凸显了情感差异。表达感受时，Claude 3 Haiku、GPT-3.5与人类使用悲伤词汇的频率均高于随机预期，但Haiku表达悲伤的情感强度低于人类（U = 1243.5，p = .036）。我们结合基于大语言模型模拟患者的心理治疗训练背景讨论了这些差异。研究结果表明，目标-事件-行动者网络能从叙事中提取相关的情感、句法和语义信息，为认知网络科学驱动的文本分析开辟新路径。