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Manuscript ID : 1404072951872 Visit : 172 Page: 27 - 37

Article Type: Original Research

Design a Novel Emotion Assessment Approach for Cancer Care Based on Large Language Models

Subject Areas : مهندسی برق و کامپیوتر

N. Fareghzadeh 1 * , M. Ghobadi 2 , P. Rahmani 3 , M. Bazargani 4

1 - Dept. of Comp. Eng., Khodabandeh Branch, Islamic Azad University, Khodabandeh, Iran
2 - Faculty of Computer Science, Electronics Department, Islamic Azad University, Tehran, Iran
3 - Faculty of Computer Science, Pardis Branch, Islamic Azad University, Pardis, Iran
4 - Dept. of Comp. Eng., Zanjan Branch, Islamic Azad University, Zanjan, Iran

Received: 2025-10-21 Accepted : 2026-02-17 Published : 2026-05-12

Keywords: Natural Language Processing, Emotion Analysis, Cancer, Large Language Models, Deep Learning,

Abstract :

 

This research initiated to address the concern and challenge of the lack of specialized native tools in the field of emotion analysis in cancer patients and to provide a new system for assessing the emotions of these patients. The main goal of this study was to design and implement a hybrid system based on deep learning to identify the presence of one or more emotions simultaneously from six emotional categories (happiness, sadness, anger, fear, hope, despair) in Persian texts on social networks. This new approach allows for a better understanding of the complexity and simultaneity of emotions. For this purpose, a set of 10,000 cancer-related posts from social platforms was tagged. The proposed model, which uses the intelligent integration of the large native language model ParsBERT with the Bi-GRU network, was fine-tuned for this task. The evaluation results clearly demonstrate the remarkable achievement of this research; the proposed method achieved an accuracy of 86.8% and an average F1-Score of 84%. The proposed system showed an improvement of 2.3% in accuracy compared to the baseline models. The system performs effectively in detecting frequent emotions such as sadness with an F1-Score of 92% and despair with an F1-Score of 90%, providing an automated and powerful tool for monitoring the emotions of cancer patients and more effective psychotherapy interventions.
 
 
 

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