Calibrating Sentiment Analysis: A Unimodal-Weighted Label Distribution Learning Approach

Label distribution learning (LDL) provides a robust framework for classification tasks exhibiting intrinsic label ordinality and ambiguity, yet its application to sentiment analysis remains underexplored. This work bridges this research gap through a comprehensive investigation of LDL for sentiment classification, revealing both strengths (predictive accuracy) and limitations (calibration reliability). To address these limitations, we propose Unimodal-Weighted LDL (UW-LDL), a novel framework that: 1) enforces unimodal label distributions via ordinality-grounded regularization, and 2) implements instance-specific calibration through adaptive weighted loss functions. Extensive experiments across five diverse benchmarks—including financial-domain datasets (AFS, FP, TFNS) and other sentiment corpora (SST-5, TSE)—demonstrate that UW-LDL outperforms state-of-the-art LDL methods and calibration techniques by significant margins in both predictive accuracy and uncertainty quantification. Furthermore, UW-LDL achieves these gains with single-pass inference, offering faster computation than ensemble-based calibration methods. Visualization analyses confirm that UW-LDL produces more discriminative and linearly separable text representations than conventional LDL approaches.