Sentiment Analysis for Healthcare Text Data: An AI Framework for Patient Feedback Understanding and Clinical Decision Support
Keywords:
sentiment analysis; healthcare text; clinical decision support; natural language processing; AI fairness; patient feedback; system architectureAbstract
The exponential growth of unstructured patient-generated text—from clinic portals, social media, post-discharge surveys, and electronic health record annotations—offers a rich substrate for understanding patient experience and informing clinical decision making. Yet harvesting actionable insights from this deluge demands a system-oriented artificial intelligence framework that goes far beyond the accuracy of any single sentiment classification algorithm. This paper presents a comprehensive architectural blueprint for sentiment analysis in healthcare that interweaves data ingestion, advanced natural language processing, ethical governance, and real-time clinical decision support integration. We examine the structural trade-offs inherent in deploying such a system across heterogeneous hospital infrastructures, discussing choices between cloud and on-premise deployment, batch versus streaming processing, and deep transformer models versus interpretable lexicon-based approaches. The discussion foregrounds fairness and robustness as constitutive elements of the framework, not post hoc add-ons, and analyzes bias propagation pathways from training data imbalance through to clinical decision dashboards. Further, we delineate the policy landscape—spanning HIPAA, GDPR, and emerging FDA guidance on artificial intelligence as a medical device—and propose governance mechanisms including model cards and human-in-the-loop auditing. Sustainability considerations, from model compression to federated learning for multi-institutional collaboration, are treated as integral to long-term viability. Through a synthesis of architectural reasoning, case illustrations, and cross-domain contrast with financial sentiment systems, the paper argues that a sociotechnical perspective is indispensable for designing sentiment analysis pipelines that are at once performant, equitable, and clinically meaningful.
References
1. Greaves, F., Ramirez-Cano, D., Millett, C., Darzi, A., & Donaldson, L. (2013). Use of sentiment analysis for capturing patient experience from free-text comments posted online. Journal of Medical Internet Research, 15(11), e239.
2. Jiménez-Zafra, S. M., Martín-Valdivia, M. T., Molina-González, M. D., & Ureña-López, L. A. (2019). How do we talk about doctors and drugs? Sentiment analysis in health-web forums. Expert Systems with Applications, 122, 138–147.
3. Korkontzelos, I., Nikfarjam, A., Shardlow, M., Sarker, A., Ananiadou, S., & Gonzalez, G. H. (2016). Analysis of the effect of sentiment analysis on extracting adverse drug events from tweets and forum posts. Journal of Biomedical Informatics, 62, 148–158.
4. Asghar, M. Z., Ahmad, S., Qasim, M., Zahra, S. R., & Kundi, F. M. (2016). Sentiment analysis on healthcare tweets: A lexicon and machine learning based approach. International Journal of Advanced Computer Science and Applications, 7(1), 510–516.
5. Kusal, S., Patil, S., Choudrie, J., Kotecha, K., Vuppalapati, C., & Abraham, A. (2022). A systematic review on sentiment analysis of health-related content in social media. International Journal of Environmental Research and Public Health, 19(2), 812.
6. 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 (pp. 4171–4186).
7. 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, 5998–6008.
8. 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.
9. Alsentzer, E., Murphy, J. R., Boag, W., Weng, W.-H., Jin, D., Naumann, T., & McDermott, M. B. A. (2019). Publicly available clinical BERT embeddings. Proceedings of the 2nd Clinical Natural Language Processing Workshop (pp. 72–78).
10. Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S., & Ramoni, R. B. (2016). SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. Journal of the American Medical Informatics Association, 23(5), 899–908.
11. Kreps, J., Narkhede, N., & Rao, J. (2011). Kafka: A distributed messaging system for log processing. Proceedings of the NetDB Workshop, 1–7.
12. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems, 28, 2503–2511.
13. Li, Q. (2026). Dynamic Adaptive Attention and Supervised Contrastive Learning: A Novel Hybrid Framework for Text Sentiment Classification. arXiv preprint arXiv:2604.10459.
14. 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.
15. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35.
16. Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. Proceedings of the Conference on Fairness, Accountability, and Transparency, 59–68.
17. Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37–43.
18. Bates, D. W., Kuperman, G. J., Wang, S., Gandhi, T., Kittler, A., Volk, L., Spurr, C., Khorasani, R., Tanasijevic, M., & Middleton, B. (2003). Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. Journal of the American Medical Informatics Association, 10(6), 523–530.
19. Kawamoto, K., Houlihan, C. A., Balas, E. A., & Lobach, D. F. (2005). Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ, 330(7494), 765.
20. Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015). Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1721–1730.
21. Chen, I. Y., Johansson, F. D., & Sontag, D. (2018). Why is my classifier discriminatory? Advances in Neural Information Processing Systems, 31, 3543–3554.
22. Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the ACM, 61(10), 36–43.
23. Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., ... & Cardoso, M. J. (2020). The future of digital health with federated learning. npj Digital Medicine, 3, 119.
24. Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Data Intelligence and AI Systems
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.
