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Personalized Care Through Sentiment Analysis and Natural Language Processing
Praveen Kumar Verma1, Abhay Bhatia2

1Praveen Kumar Verma, Assistant Professor, Department of Computer Science and Engineering, Kunwar Satya Vira College of Engineering & Management, Bijnor (Uttar Pradesh), India.

2Dr. Abhay Bhatia, Associate Professor, Department of Computer Science and Engineering, Roorkee Institute of Technology, Roorkee, Haridwar (Uttarakhand), India. 

Manuscript received on 29 October 2024 | First Revised Manuscript received on 11 November 2024 | Second Revised Manuscript received on 07 December 2024 | Manuscript Accepted on 15 January 2025 | Manuscript published on 30 January 2025 | PP: 5-11 | Volume-14 Issue-6, January 2025 | Retrieval Number: 100.1/ijsce.F365714060125 | DOI: 10.35940/ijsce.F3657.14060125

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In hospitals and other healthcare organizations, understanding patient feedback helps to exceed in providing top-notch care. Sentiment analysis to enhance patient care is the way to know how patients feel about different service aspects, including processes, infrastructure, treatment, and healthcare professionals. Enhancing healthcare with sentiment analysis means removing human bias through consistent analysis, gaining real-time insights about patient satisfaction, and improving standards of care by incorporating patient feedback. In this paper, we will examine several facets of utilizing sentiment analysis for patient happiness, such as the various forms of sentiment analysis, its applications in healthcare, and its precise methodology. This work intends to guide algorithm selection and progress NLP research by adding to the continuing conversation on advancing sentiment analysis in the context of big data and computational linguistics. These results highlight the adaptability of NLP methods and their potential to enhance patient outcomes, research, and healthcare delivery.

Keywords: NLP, SVM, Machine Learning, Emotions.
Scope of the Article: Machine and Knowledge Learning