Improving Sentiment Classification Using 0-Shot Generated Labels for Custom Transformer Embeddings
Bibliographic record
Abstract
In this article, we present an approach to enrich transformers with additional information for general classification tasks given a set of relevant helper labels. We investigate whether the addition of preselected emotions as relevant helper labels can improve sentiment classification using BERT and DistilBERT. This method generates zero-shot labels like emotions for sentiment, and uses them as auxiliary text and classifier inputs to contribute to the final sentiment prediction. The approach has shown improvements in F1 score primarily for small datasets (1,000–50,000 samples). We also found that large, difficult-to-improve datasets such as the Sentiment140 dataset, with 1.6 million samples, also benefited from our approach. We tested the improvements on a smaller dataset, specifically an airline dataset comprising over 11,500 samples, and on subsamples of the Sentiment140 dataset with sizes ranging from 500 to 50,000. We conducted an ablation study on the zero-shot labels, which indicated that more labels generally improve the model. Our results show improvements in all cases over the original model for both BERT and DistilBERT when tested with added emotion inputs generated from zero-shot pretrained models.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".