Emotion-RGC net: A novel approach for emotion recognition in social media using RoBERTa and Graph Neural Networks
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| Publicado en: | PLoS One vol. 20, no. 3 (Mar 2025), p. e0318524 |
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| Otros Autores: | , |
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Public Library of Science
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0318524 |2 doi | |
| 035 | |a 3173338515 | ||
| 045 | 2 | |b d20250301 |b d20250331 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Jiangting Yan | |
| 245 | 1 | |a Emotion-RGC net: A novel approach for emotion recognition in social media using RoBERTa and Graph Neural Networks | |
| 260 | |b Public Library of Science |c Mar 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Emotion recognition in social media is a challenging task due to the complex and unstructured nature of user-generated content. In this paper, we propose Emotion-RGC Net, a novel deep learning model that integrates RoBERTa, Graph Neural Networks (GNN), and Conditional Random Fields (CRF) to enhance the accuracy and robustness of emotion classification. RoBERTa is employed for effective feature extraction from unstructured text, GNN captures the propagation and influence of emotions through user interactions, and CRF ensures global consistency in emotion label prediction. We evaluate the proposed model on two widely-used datasets, Sentiment140 and Emotion, demonstrating significant improvements over traditional machine learning models and other deep learning baselines in terms of accuracy, recall, F1-score, and AUC. Emotion-RGC Net achieves an accuracy of 89.70% on Sentiment140 and 88.50% on Emotion, highlighting its effectiveness in handling both coarse- and fine-grained emotion classification tasks. Despite its strong performance, we identify areas for future research, including reducing the model’s reliance on large labeled datasets, improving computational efficiency, and incorporating temporal dynamics to capture emotion evolution in social networks. Our results indicate that Emotion-RGC Net provides a robust solution for emotion recognition in diverse social media contexts. | |
| 653 | |a Language | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Classification | ||
| 653 | |a Social networks | ||
| 653 | |a Conditional random fields | ||
| 653 | |a Social network analysis | ||
| 653 | |a Neural networks | ||
| 653 | |a Machine learning | ||
| 653 | |a Emotions | ||
| 653 | |a Retinal ganglion cells | ||
| 653 | |a User generated content | ||
| 653 | |a Natural language | ||
| 653 | |a Propagation | ||
| 653 | |a Datasets | ||
| 653 | |a Social organization | ||
| 653 | |a Emotion recognition | ||
| 653 | |a Graph neural networks | ||
| 653 | |a Effectiveness | ||
| 653 | |a Unstructured data | ||
| 653 | |a Social interactions | ||
| 653 | |a Digital media | ||
| 653 | |a Social | ||
| 700 | 1 | |a Pu, Pengju | |
| 700 | 1 | |a Jiang, Liheng | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 3 (Mar 2025), p. e0318524 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3173338515/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3173338515/fulltext/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3173338515/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |