Knowledge-Enhanced Sentiment Analysis for Biomedical and Clinical Text
Keywords:
sentiment analysis; biomedical text; clinical natural language processing; knowledge graphs; domain adaptation; interpretability; governanceAbstract
Sentiment analysis has become a widely used computational method for extracting evaluative meaning from text, but its application to biomedical and clinical corpora requires substantially more than generic polarity classifiers. Biomedical text contains specialized terminology, negated expressions, hedged clinical language, and patient-generated forms that resist standard sentiment lexicons and models trained on open-domain reviews. This paper examines knowledge-enhanced sentiment analysis for biomedical and clinical text from a systems perspective. It explores how external knowledge resources such as biomedical ontologies, semantic networks, and domain-specific language representations can be coupled with modern attention-based architectures to improve contextual interpretation. Rather than focusing solely on model construction, the paper discusses structural trade-offs among knowledge injection strategies, domain adaptation, interpretability, computational cost, and robustness. It further addresses governance considerations including data privacy, fairness, clinical validity, and regulatory alignment. Deployment infrastructures, monitoring mechanisms, and sustainability are analyzed as integral parts of the socio-technical system. The paper also identifies open challenges and future research directions, including dynamic knowledge updates, multilingual and multimodal integration,and longitudinal clinical narrative understanding are highlighted as central research directions for the next generation of sentiment-aware clinical informatics.
References
1. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
2. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167.
3. Demner-Fushman, D., Chapman, W. W., & McDonald, C. J. (2009). What can natural language processing do for clinical decision support? Journal of Biomedical Informatics, 42(5), 760-772.
4. Uzuner, Ö., South, B. R., Shen, S., & DuVall, S. L. (2011). 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. Journal of the American Medical Informatics Association, 18(5), 552-556.
5. Friedman, C., Rindflesch, T. C., & Corn, M. (2013). Natural language processing: State of the art and prospects for significant progress, a workshop sponsored by the National Library of Medicine. Journal of Biomedical Informatics, 46(5), 765-773.
6. Bodenreider, O. (2004). The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Research, 32(suppl_1), D267-D270.
7. Aronson, A. R. (2001). Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proceedings of the AMIA Symposium, 17-21.
8. Donnelly, K. (2006). SNOMED-CT: The advanced terminology and coding system for eHealth. Studies in Health Technology and Informatics, 121, 279-290.
9. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
10. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4171-4186.
11. Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234-1240.
12. Alsentzer, E., Murphy, J. R., Boag, W., Weng, W.-H., Jin, D., Naumann, T., & McDermott, M. (2019). Publicly available clinical BERT embeddings. Proceedings of the 2nd Clinical Natural Language Processing Workshop, 72-78.
13. Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2021). Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare, 3(1), 1-23.
14. Zunic, A., Corcoran, P., & Spasic, I. (2020). Sentiment analysis in health and well-being: Systematic review. JMIR Medical Informatics, 8(1), e16023.
15. Li, Q. (2026). Dynamic Adaptive Attention and Supervised Contrastive Learning: A Novel Hybrid Framework for Text Sentiment Classification. arXiv preprint arXiv:2604.10459.
16. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135-1144.
17. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
18. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
19. Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018). Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine, 169(12), 866-872.
20. European Parliament and Council of the European Union. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Official Journal of the European Union, L119, 1-88.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Advanced Artificial Intelligence Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
