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A Comparative Study of X Data About the NHS Using Sentiment Analysis

Saeed Ur Rehman Orcid Logo, Obi Oluchi Blessing, Anwar Ali Orcid Logo

Big Data and Cognitive Computing, Volume: 9, Issue: 10, Start page: 244

Swansea University Author: Anwar Ali Orcid Logo

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DOI (Published version): 10.3390/bdcc9100244

Abstract

This study investigates sentiment analysis of X data about the National Health Service (NHS) during a politically charged period, using lexicon-based, machine learning, and deep learning approaches, as well as topic modelling and aspect-based sentiment analysis (ABSA). This study is distinct in its...

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Published in: Big Data and Cognitive Computing
ISSN: 2504-2289
Published: MDPI AG 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa70566
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spelling 2025-10-03T11:17:37.4918105 v2 70566 2025-10-03 A Comparative Study of X Data About the NHS Using Sentiment Analysis f206105e1de57bebba0fd04fe9870779 0000-0001-7366-9002 Anwar Ali Anwar Ali true false 2025-10-03 ACEM This study investigates sentiment analysis of X data about the National Health Service (NHS) during a politically charged period, using lexicon-based, machine learning, and deep learning approaches, as well as topic modelling and aspect-based sentiment analysis (ABSA). This study is distinct in its comparative evaluation of sentiment analysis techniques on NHS-related tweets during a politically sensitive period, offering insights into public opinion shaped by political discourse. A dataset of 35,000 tweets collected and analysed using various techniques, including VADER, TextBlob, Naive Bayes, Support Vector Machines, Logistic Regression, Ensemble Learning, and BERT. Unlike previous studies that focus on structured feedback or general sentiment, this research uniquely explores unstructured public discourse during an election period, capturing real-time political sentiment towards NHS policies. The sentiment distribution from lexicon-based methods depicted that the presence of stop words could affect model performance. While all models achieved high accuracy on the validation dataset, challenges such as class imbalance and limited labelled data impacted performance, with signs of overfitting observed. Topic modelling identified nine topic clusters, with “waiting list,” “service,” and “immigration” carrying negative sentiments. At the same time, words like “thank,” “support,” “care,” and “team” had the most positive sentiments, reflecting public delight in these areas. ABSA identified positive sentiments towards aspects like “useful service”. This study contributes a comparative framework for evaluating sentiment analysis techniques in politically contextualised healthcare discourse, offering insights for policymakers and researchers. The study underscores the importance of data quality in sentiment analysis. Future research should consider incorporating multilingual datasets, extending data collection periods, optimising deep learning models, and employing hybrid approaches to enhance performance. Journal Article Big Data and Cognitive Computing 9 10 244 MDPI AG 2504-2289 sentiment analysis; NHS; social media; machine learning; BERT 24 9 2025 2025-09-24 10.3390/bdcc9100244 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University Other 2025-10-03T11:17:37.4918105 2025-10-03T10:54:05.0412818 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering Saeed Ur Rehman 0009-0009-4566-7144 1 Obi Oluchi Blessing 2 Anwar Ali 0000-0001-7366-9002 3 70566__35233__0d0366078e7a4fc0a30b9b984275df44.pdf 70566.VOR.pdf 2025-10-03T10:57:48.6114959 Output 2647850 application/pdf Version of Record true © 2025 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. true eng https://creativecommons.org/licenses/by/4.0/
title A Comparative Study of X Data About the NHS Using Sentiment Analysis
spellingShingle A Comparative Study of X Data About the NHS Using Sentiment Analysis
Anwar Ali
title_short A Comparative Study of X Data About the NHS Using Sentiment Analysis
title_full A Comparative Study of X Data About the NHS Using Sentiment Analysis
title_fullStr A Comparative Study of X Data About the NHS Using Sentiment Analysis
title_full_unstemmed A Comparative Study of X Data About the NHS Using Sentiment Analysis
title_sort A Comparative Study of X Data About the NHS Using Sentiment Analysis
author_id_str_mv f206105e1de57bebba0fd04fe9870779
author_id_fullname_str_mv f206105e1de57bebba0fd04fe9870779_***_Anwar Ali
author Anwar Ali
author2 Saeed Ur Rehman
Obi Oluchi Blessing
Anwar Ali
format Journal article
container_title Big Data and Cognitive Computing
container_volume 9
container_issue 10
container_start_page 244
publishDate 2025
institution Swansea University
issn 2504-2289
doi_str_mv 10.3390/bdcc9100244
publisher MDPI AG
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering
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description This study investigates sentiment analysis of X data about the National Health Service (NHS) during a politically charged period, using lexicon-based, machine learning, and deep learning approaches, as well as topic modelling and aspect-based sentiment analysis (ABSA). This study is distinct in its comparative evaluation of sentiment analysis techniques on NHS-related tweets during a politically sensitive period, offering insights into public opinion shaped by political discourse. A dataset of 35,000 tweets collected and analysed using various techniques, including VADER, TextBlob, Naive Bayes, Support Vector Machines, Logistic Regression, Ensemble Learning, and BERT. Unlike previous studies that focus on structured feedback or general sentiment, this research uniquely explores unstructured public discourse during an election period, capturing real-time political sentiment towards NHS policies. The sentiment distribution from lexicon-based methods depicted that the presence of stop words could affect model performance. While all models achieved high accuracy on the validation dataset, challenges such as class imbalance and limited labelled data impacted performance, with signs of overfitting observed. Topic modelling identified nine topic clusters, with “waiting list,” “service,” and “immigration” carrying negative sentiments. At the same time, words like “thank,” “support,” “care,” and “team” had the most positive sentiments, reflecting public delight in these areas. ABSA identified positive sentiments towards aspects like “useful service”. This study contributes a comparative framework for evaluating sentiment analysis techniques in politically contextualised healthcare discourse, offering insights for policymakers and researchers. The study underscores the importance of data quality in sentiment analysis. Future research should consider incorporating multilingual datasets, extending data collection periods, optimising deep learning models, and employing hybrid approaches to enhance performance.
published_date 2025-09-24T08:27:49Z
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