MultiGranular Sentiment Intensity Augmentation with Ordinal Contrastive Learning for Enhanced Sentiment Classification
Bibliographic record
Abstract
Current sentiment analysis approaches rely on coarse-grained classification, failing to capture the nuanced intensity variations that characterize human emotional expression. We propose Multi-Granular Sentiment Intensity Augmentation (MGSIA), a systematic framework that generates five-level sentiment intensity spectra using Large Language Models (LLMs) and employs ordinal contrastive learning to preserve intensity relationships in embedding space.MGSIA combines three key innovations: (1) LLM-based intensity-aware data augmentation that generates semantically coherent sentiment variations, (2) ordinal contrastive learning that respects intensity hierarchies, and (3) a unified loss function integrating classification, ordinal consistency, and contrastive objectives.Comprehensive experiments on three benchmark datasets (SST-5, IMDB, and Yelp) demonstrate consistent improvements over state-of-the-art methods, with an average accuracy gain of 2.3% across all datasets. MGSIA achieves particularly strong performance on challenging neutral sentiment classification (+5.6% improvement) and fine-grained sentiment tasks (+3.1% on SST-5). Ablation studies confirm that each component contributes meaningfully, with the complete framework yielding a total 5.2% accuracy improvement over baseline approaches. Our approach addresses a gap in sentiment analysis by providing both robust classification performance and interpretable intensity-aware predictions for enhanced sentiment understanding.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".