Intermediate Fine-Tuning for Robust Persian Emotion Detection in Text

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Mahdavi Mortazavi Morteza; ShamsFard Mehrnoush

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Journal Paper

Paper Information

Journal: JOURNAL OF INNOVATIONS IN COMPUTER SCIENCE AND ENGINEERING Year:2024 | Volume:2 | Issue:1 Page(s): 1-6

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Information Journal Paper

Title

Intermediate Fine-Tuning for Robust Persian Emotion Detection in Text

Author(s)

Mahdavi Mortazavi Morteza | ShamsFard Mehrnoush | Issue Writer Certificate

Keywords

Keywords: Emotion Recognition

Persian Text Processing

Intermediate Fine-Tuning

BERT-based Models

NLP in Low-resource Languages

Abstract

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 textvariations, 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...

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