同时识别电商评论中的情绪与触发源，更懂用户真实感受。
Why We Feel What We Feel: Joint Detection of Emotions and Their Opinion Triggers in E-commerce
- 构建情绪与观点触发点联合检测任务，基于普拉切克8类基本情绪理论。
- 提出EOT-X数据集，含2400条人工标注的电商评论，标注细粒度情绪与触发段落。
- 设计结构化提示框架EOT-DETECT，显著优于零样本与思维链方法。
电商平台的用户评论包含影响购买决策的关键情感信号。然而，现有研究尚未探索在电商评论中联合进行情绪识别与解释性文本段落（观点触发点）定位的任务，这构成了理解用户情感反应成因的重要空白。为填补这一空白，我们提出一项新联合任务——情绪检测与观点触发提取（EOT），该任务显式建模因果文本段落（观点触发点）与情感维度（情绪类别）之间的关系，其理论基础源自普拉切克的8种基本情绪理论。在缺乏标注数据的情况下，我们构建了EOT-X，一个包含2,400条人工标注的电商评论数据集，涵盖细粒度情绪标签与观点触发段落。我们评估了23个大语言模型，并提出EOT-DETECT，一种具有系统性推理与自我反思能力的结构化提示框架。该框架在多个电商领域上均超越零样本与思维链方法的表现。
原文摘要 · Abstract (English)
Customer reviews on e-commerce platforms capture critical affective signals that drive purchasing decisions. However, no existing research has explored the joint task of emotion detection and explanatory span identification in e-commerce reviews - a crucial gap in understanding what triggers customer emotional responses. To bridge this gap, we propose a novel joint task unifying Emotion detection and Opinion Trigger extraction (EOT), which explicitly models the relationship between causal text spans (opinion triggers) and affective dimensions (emotion categories) grounded in Plutchik's theory of 8 primary emotions. In the absence of labeled data, we introduce EOT-X, a human-annotated collection of 2,400 reviews with fine-grained emotions and opinion triggers. We evaluate 23 Large Language Models (LLMs) and present EOT-DETECT, a structured prompting framework with systematic reasoning and self-reflection. Our framework surpasses zero-shot and chain-of-thought techniques, across e-commerce domains.
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