Intermediate Fine-Tuning for Robust Persian Emotion Detection in Text | ||
| Journal of Innovations in Computer Science and Engineering (JICSE) | ||
| مقاله 12، دوره 2، Special Issue on AI 4 All - 1، شهریور 2024، صفحه 1-6 اصل مقاله (502.92 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.48308/jicse.2025.239714.1065 | ||
| نویسندگان | ||
| Seyed Morteza Mahdavi Mortazavi* ؛ Mehrnoush ShamsFard | ||
| Faculty of Computer Science and Engineering Shahid Beheshti University | ||
| چکیده | ||
| Emotion recognition in text is a growing area in Natural Language Processing ( NLP ), essential for improving human-computer interactions by allowing systems to interpret emotional expressions. While much progress has been made in English, Persian emotion recognition has seen limited devel- opment due to resource constraints and linguistic challenges. In this study, we address these gaps by leveraging two key Persian datasets, ArmanEmo and ShortEmo, to train an efficient emotion recognition model. Using FaBERT, a BERT-based model optimized for Persian, we employ interme- diate fine-tuning on a large collection of informal and formal Persian texts to enhance the model’s adaptability to colloquial language. This step significantly improves comprehension of Persian text variations, as reflected in reduced perplexity scores. Our final evaluations, incorporating accuracy, precision, recall, and F1 score metrics, demonstrate that this fine-tuned FaBERT model achieves strong performance in emotion recognition, providing a promising approach for NLP in low-resource languages... | ||
| کلیدواژهها | ||
| Keywords: Emotion Recognition؛ Persian Text Processing؛ Intermediate Fine-Tuning؛ BERT-based Models؛ NLP in Low-resource Languages | ||
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