Intermediate Fine-Tuning for Robust Persian Emotion Detection in Text

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 development 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 intermediate 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 F۱ 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.

کلیدواژه ها:

Emotion Recognition ، Persian Text Processing ، Intermediate Fine-Tuning ، BERT-based Models ، NLP in Low-resource Languages