GAN-BElectra: Enhanced Multi-class Sentiment Analysis with Limited Labeled Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Performing sentiment analysis with high accuracy using machine-learning techniques requires a large quantity of training data. However, getting access to such a large quantity of labeled data for specific domains can be expensive and time-consuming. These warrant developing more efficient techniques that can perform sentiment analysis with high accuracy with a few labeled training data. In this paper, we aim to address this problem with our proposed novel sentiment analysis technique, named GAN-BElectra. With rigorous experiments, we demonstrate that GAN-BElectra outperforms its baseline technique in terms of multiclass sentiment analysis accuracy with a few labeled data while maintaining an architecture with reduced complexity compared to its predecessor.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it