Bibliographic record
Abstract
Sentiment classification, as an essential task for understanding the sentiment content of texts, has garnered extensive attention. However, existing methods face numerous challenges, such as the mismatch between training and testing data in specific domains, which results in insufficient adaptability of models to new domains. Prompt learning is a parameter-efficient strategy that effectively enables pre-trained models to adapt to downstream tasks. In this paper, we propose a novel two-stage method called PT2, which consists of two key components: prompt pre-training and prompt fine-tuning. During the prompt pre-training phase, we employ masked language modeling and cross-domain prompt learning, aiming to uncover latent sentiments and acquire cross-domain knowledge. In the prompt fine-tuning phase, we apply the pre-trained model to specific cross-domain sentiment classification tasks, adjusting the model parameters through additional sentiment prompt templates to improve its performance on these specific tasks. We conducted experiments on publicly available datasets and established nine different cross-domain settings. The results indicate that the model, employing our proposed $\mathrm{PT}^{2}$, significantly outperforms previous state-of-the-art approaches in cross-domain sentiment classification tasks.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".