Towards Interpretable Emotion Classification: Evaluating LIME, SHAP, and Generative AI for Decision Explanations
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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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".