🤖 AI Summary
This study addresses the substantial inter-annotator disagreement and low stability observed in sentiment annotation. Grounded in Appraisal Theory, it systematically investigates the textual features of sentiment expression and their computability. Using a manually annotated industrial corpus, we construct a fine-grained sentiment dataset and train language models to emulate human annotation behavior. Methodologically, we move beyond mainstream sentiment classification paradigms by incorporating appraisal-oriented semantic structures—namely, Attitude, Engagement, and Graduation—which remain underexplored in computational linguistics. This enables us to uncover stable statistical patterns and language-driven mechanisms underlying annotation discrepancies. Experiments demonstrate that our model effectively discriminates among distinct appraisal-based sentiment contexts, achieving significant improvements over baselines in cross-context generalization and fine-grained linguistic cue modeling. Our work establishes a theory-informed modeling framework for sentiment computation and provides an interpretable, appraisal-grounded evaluation benchmark.
📝 Abstract
Emotion is a crucial phenomenon in the functioning of human beings in society. However, it remains a widely open subject, particularly in its textual manifestations. This paper examines an industrial corpus manually annotated following an evaluative approach to emotion. This theoretical framework, which is currently underutilized, offers a different perspective that complements traditional approaches. Noting that the annotations we collected exhibit significant disagreement, we hypothesized that they nonetheless follow stable statistical trends. Using language models trained on these annotations, we demonstrate that it is possible to model the labeling process and that variability is driven by underlying linguistic features. Conversely, our results indicate that language models seem capable of distinguishing emotional situations based on evaluative criteria.