From Statistical Features to Contextual Embeddings: A Multi-Paradigm Evaluation of Emotion Classification Models
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| Format: | Recurso digital |
| Sprache: | Englisch |
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Zenodo
2026
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| _version_ | 1866901957494439936 |
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| author | Gregorius, Reynaldi Pratama |
| author_facet | Gregorius, Reynaldi Pratama |
| contents | <p>This study evaluates multiple model variants across all three paradigms: classical machine learning with statistical feature extraction (Naive Bayes, Logistic Regression, LinearSVC, XGBoost, MLP), deep learning with pre-trained word embeddings (Bidirectional GRU and LSTM with GloVe and FastText), and fine-tuned transformer language models (BERT, RoBERTa, DistilBERT). All models are evaluated on a six-class emotion dataset of 20,000 samples under identical experimental conditions. Results show that the transition from statistical features to GloVe-based embeddings produces the largest single performance gain (+3.6 F1 points), while full transformer fine-tuning yields only a marginal additional improvement (+0.8 points) at substantially higher computational cost. The study provides practical model selection guidance for teams working under real-world resource constraints.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19028571 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | From Statistical Features to Contextual Embeddings: A Multi-Paradigm Evaluation of Emotion Classification Models Gregorius, Reynaldi Pratama emotion classification natural language processing transformer fine-tuning word embeddings text classification <p>This study evaluates multiple model variants across all three paradigms: classical machine learning with statistical feature extraction (Naive Bayes, Logistic Regression, LinearSVC, XGBoost, MLP), deep learning with pre-trained word embeddings (Bidirectional GRU and LSTM with GloVe and FastText), and fine-tuned transformer language models (BERT, RoBERTa, DistilBERT). All models are evaluated on a six-class emotion dataset of 20,000 samples under identical experimental conditions. Results show that the transition from statistical features to GloVe-based embeddings produces the largest single performance gain (+3.6 F1 points), while full transformer fine-tuning yields only a marginal additional improvement (+0.8 points) at substantially higher computational cost. The study provides practical model selection guidance for teams working under real-world resource constraints.</p> |
| title | From Statistical Features to Contextual Embeddings: A Multi-Paradigm Evaluation of Emotion Classification Models |
| topic | emotion classification natural language processing transformer fine-tuning word embeddings text classification |
| url | https://doi.org/10.5281/zenodo.19028571 |