Calibrating Sentiment Analysis: A Unimodal-Weighted Label Distribution Learning Approach
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".