Why We Feel What We Feel: Joint Detection of Emotions and Their Opinion Triggers in E -commerce (2025)

Abstract: 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.

## introduction emotional content in customer reviews fundamentally drives consumer behavior @xcite demonstrates their direct influence on purchasing decisions, while @xcite establishes their impact on post-purchase satisfaction. these emotional expressions powerfully shape how future consumers perceive and engage with products @xcite @xcite @xcite . despite these clear benefits, no existing research in literature has explored the joint task of emotion detection and explanatory span identification in e-commerce reviews, leaving a significant gap in ♢ equal contribution understanding what customers feel and why they feel it. for instance, merely detecting "disgust" in a review provides limited value compared to identifying that this emotion stems from specific triggers explicitly stated by customers, such as finding hair strands in makeup products or discovering expired skincare items. we address this gap by introducing emotion detection and opinion trigger extraction (eot), a joint task that directly models the bidirectional relationship between emotions and their corresponding opinion triggers, grounded in plutchik's theory @xcite . ## task formulation we define the key components as: ## methodology our methodology evaluates three prompting strategies of incrementally increasing complexity, as illustrated in figure 2 . we begin with a zero-shot (zs) prompt that establishes a baseline with a basic task description. we then build on this with a zero-shot chain-of-thought (zs-cot) prompt, which encourages the model to generate its own reasoning steps. finally, we introduce our proposed eot-detect framework, which provides a complete, multi-step reasoning and self-reflection sequence. this incremental design allows us to systematically evaluate the benefits of structured prompting through a clear, strategy-level comparison. ## dataset we utilized multi-domain dataset @xmath0, tripadvisor, yelp}, we employed simple random sampling without replacement (srswor) @xcite . the amazon subset (from amazon reviews '23; @xcite ) spans subdomains @xmath1, home, electronics, clothing}. let p s denote products in subdomain @xmath2. we sample n @xmath3: p i ∼ srswor(p s , n s ), ∀@xmath4, yielding @xmath5. for tripadvisor @xcite and yelp (yelp, 2025) , we sampled n @xmath6. for each product p, we define a length-filtered review set:r @xmath7. we then sample @xmath8: r * p ∼ srswor(r p , m) s.t. ∀@xmath9: ## results summary our comprehensive evaluation-based on a rigorously aggregated gold standard-shows that: (1) model families differ markedly in their baseline and enhanced performance. (2) the eot-detect framework delivers consistent and significant gains across a wide range of models compared to standard methods. (3 larger parameter sizes generally translate to improved performance, with architectural design playing a critical role. (4) although closed-source models currently hold a performance edge, open-source models are rapidly catching up. these findings underscore the importance of structured prompt engineering, model scale, and aggregation methodologies in advancing the state of emotion and trigger detection in natural language processing. ## conclusion & future work this work introduced eot (emotion-opinion trigger), the first joint task that unifies emotion detection and opinion trigger extraction in e-commerce reviews. we demonstrated why a focused set of plutchik's 8 primary emotions provides the optimal framework for analyzing customer feedback, supported by empirical evidence of strong inter-annotator agreement (emotion κ = 0.88, trigger κ = 0.84). some key takeaways are: (a) our work presents the eot-x benchmark-a rigorously annotated dataset. (b) eot-detect, a structured prompting framework that achieves superior performance over conventional zero-shot and chain-of-thought methods. (c) eot-llama, an edgedeployable 1b-parameter model that outperforms larger models while maintaining efficiency. our work aims to establish a foundation for understanding not just what emotions customers express, but why they feel them, enabling more nuanced analysis of user feedback. we hope our work catalyzes further advances in emotion-opinion analysis systems for e-commerce applications.