Why We Feel What We Feel: Joint Detection of Emotions and Their Opinion Triggers in E-commerce
This addresses a gap in understanding customer emotional triggers for e-commerce platforms, though it is incremental as it builds on existing emotion detection and opinion mining tasks.
The paper tackles the joint task of detecting emotions and identifying their explanatory triggers in e-commerce reviews, introducing a new dataset and a prompting framework that outperforms existing methods across domains.
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.