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A Comparative Study of X Data About the NHS Using Sentiment Analysis
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Obi Oluchi Blessing,
Anwar Ali
Big Data and Cognitive Computing, Volume: 9, Issue: 10, Start page: 244
Swansea University Author:
Anwar Ali
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© 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.
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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...
| Published in: | Big Data and Cognitive Computing |
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| ISSN: | 2504-2289 |
| Published: |
MDPI AG
2025
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa70566 |
| 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 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. |
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| Keywords: |
sentiment analysis; NHS; social media; machine learning; BERT |
| College: |
Faculty of Science and Engineering |
| Issue: |
10 |
| Start Page: |
244 |