Towards Interpretable Emotion Classification: Evaluating LIME, SHAP, and Generative AI for Decision Explanations
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
This paper explores the classification of multi-label emotions utilizing fine-tuned RoBERTa base and zero-shot GPT4 models, with experiments conducted on the SemEval 2018 E-c dataset encompassing 11 emotions, where more than one label is allowed for a text. Employing SHAP and LIME for RoBERTa explanations and generative AI for GPT4, we assess the sufficiency of explanations using the BERT score metric. We show the explanations generated by LIME and SHAP visually using different plots. The BERT score indicates that generative AI produces better explanations than the statistical models, providing deeper insights into emotion selection, with a BERT score of 59.66% compared to SHAP-RoBERTa's 54.17% and LIME-RoBERTa's 53.22%. This shows the potential of generative AI in revealing the reasoning behind decisions within complex emotional contexts. Though the performance is superior, we also discuss the limitations of these models that hinder wide-scale adoption.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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