Opinion summarization systems aggregate customer sentiments without capturing the emotional factors that drive purchasing decisions, resulting in shallow summaries that overlook the affective dimensions shaping customer experiences and fail to explain why customers feel the way they do. This gap exists because prior research has neglected the interplay between expressed opinions and their underlying emotional contexts. To bridge this gap, we introduce Emotion-Aware Opinion Summarization (EAOS), a framework leveraging Large Language Models (LLMs) to integrate emotional dimensions into opinion summaries, moving beyond conventional sentiment polarity. To support this task, we develop a large-scale (40K product–summary pairs) training dataset, an evaluation benchmark, a compact 1B-parameter model that matches 70B-scale performance via knowledge distillation, and methods for generating and evaluating emotion-aware summaries. A user study shows that 82% of readers prefer our emotion-aware summaries (p < .001), confirming that adding emotion helps in making purchase decisions.

Multi-source Opinion Summarization (M-OS) extends beyond traditional opinion summarization by incorporating additional sources of product metadata such as descriptions, key features, specifications, and ratings, alongside reviews. This integration results in comprehensive summaries that capture both subjective opinions and objective product attributes essential for informed decision-making. While Large Language Models (LLMs) have shown significant success in various Natural Language Processing (NLP) tasks, their potential in M-OS remains largely unexplored. Additionally, the lack of evaluation datasets for this task has impeded further advancements. To bridge this gap, we introduce M-OS-EVAL, a benchmark dataset for evaluating multi-source opinion summaries across seven key dimensions: fluency, coherence, relevance, faithfulness, aspect coverage, sentiment consistency, and specificity. Our results demonstrate that M-OS significantly enhances user engagement, as evidenced by a user study in which, on average, 87% of participants preferred M-OS over opinion summaries. Our experiments demonstrate that factually enriched summaries enhance user engagement. Notably, M-OS-PROMPTS exhibit stronger alignment with human judgment, achieving an average Spearman correlation of ρ = 0.74, which surpasses the performance of previous methodologies.

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.