Healthcare sentiment analysis uses machine learning and natural language processing techniques to understand emotional tone, opinions, and attitudes around healthcare-related topics. This study examines patient attitudes on Twitter in Arabic, specifically regarding delays and late arrivals. The study uses machine learning, deep learning, ensemble learning, statistical analysis, and assessment methodologies to analyze tweets from January 1, 2020 to January 1, 2024 with Arabic keywords. We chose support vector machine (SVM) as a classical machine learning algorithm for supervised learning (SA) due to its efficacy and simplicity. The pre-training data set was used to investigate patient experience in Saudi Arabian hospitals. The results provide valuable insights into patient mood, allowing companies to identify areas for improvement and tailor their approach. The study improves sentiment analysis methodologies in the healthcare sector, allowing for more precise and effective analysis of patient experiences on social media sites.

sentiment analysis, natural language processing, deep learning, patient experience, machine learning, support vector machine

Received: May 7, 2024; Accepted: May 16, 2024; Published: June 3, 2024

How to cite this article: Razan S. Alanazi and Hanan Ali Alshaher, Sentiment analysis of patient experience, JP Journal of Biostatistics 24(2) (2024), 335-370. https://doi.org/10.17654/0973514324020

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

References:
[1] S. Binkheder, R. N. Aldekhyyel, A. AlMogbel, N. Al-Twairesh, N. Alhumaid, S. N. Aldekhyyel and A. A. Jamal, Public perceptions around Mhealth applications during Covid-19 pandemic: a network and sentiment analysis of tweets in Saudi Arabia, International Journal of Environmental Research and Public Health 18(24) (2021), 13388.
[2] A. Al-Hashedi, B. Al-Fuhaidi, A. M. Mohsen, Y. Ali, H. A. Gamal Al-Kaf, W. Al-Sorori and N. Maqtary, Ensemble classifiers for Arabic sentiment analysis of social network (Twitter data) towards COVID-19-related conspiracy theories, Applied Computational Intelligence and Soft Computing 2022 (2022), 1-10.
[3] B. AlBadani, R. Shi and J. Dong, A novel machine learning approach for sentiment analysis on Twitter incorporating the universal language model fine-tuning and SVM, Applied System Innovation 5(1) (2022), 13.
[4] J. E. Tang, V. Arvind, C. Dominy, C. A. White, S. K. Cho and J. S. Kim, How are patients reviewing spine surgeons online? A sentiment analysis of physician review website written comments, Global Spine Journal 13 (2023), 2107-2114. https://doi.org/10.1177/21925682211069933.
[5] M. Stemmer, Y. Parmet and G. Ravid, What are IBD patients talking about on twitter? Using natural language understanding to investigate patients’ tweets, SN Computer Science 4(4) (2023), 343.
[6] A. R. Pandey, M. Seify, U. Okonta and A. Hosseinian-Far, Advanced sentiment analysis for managing and improving patient experience: application for general practitioner (GP) classification in Northamptonshire, International Journal of Environmental Research and Public Health 20(12) (2023), 6119.
[7] S. Chekijian, H. Li and S. Fodeh, Emergency care and the patient experience: using sentiment analysis and topic modeling to understand the impact of the COVID-19 pandemic, Health and Technology 11(5) (2021), 1073-1082.
[8] M. Khanbhai, L. Warren, J. Symons, K. Flott, S. Harrison-White, D. Manton and E. Mayer, Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care, International Journal of Medical Informatics 157 (2022), 104642.
[9] J. Liu, J. Kong and X. Zhang, Study on differences between patients with physiological and psychological diseases in online health communities: topic analysis and sentiment analysis, International Journal of Environmental Research and Public Health 17(5) (2020), 1508.
[10] X. Liu, S. Zhou and X. Chi, How do team-level and individual-level linguistic styles affect patients’ emotional well-being - evidence from online doctor teams, International Journal of Environmental Research and Public Health 20(3) (2023), 1915.
[11] K. C. Wood, J. J. Bertram, T. D. Kendig and M. Pergolotti, Understanding patient experience with outpatient cancer rehabilitation care, Healthcare 11(3) (2023), 348.
[12] F. Greaves, D. Ramirez-Cano, C. Millett, A. Darzi and L. Donaldson, Use of sentiment analysis for capturing patient experience from free-text comments posted online, Journal of Medical Internet Research 15(11) (2013), e2721.
[13] C. Li, J. Fu, J. Lai, L. Sun, C. Zhou, W. Li and Y. Wu, Construction of an emotional lexicon of patients with breast cancer: development and sentiment analysis, Journal of Medical Internet Research 25 (2023), e44897.
[14] A. I. A. Rahim, M. I. Ibrahim, S. L. Chua and K. I. Musa, Hospital Facebook reviews analysis using a machine learning sentiment analyzer and quality classifier, Healthcare 9(12) (2021), 1679.
[15] S. Robinson and E. Vicha, Twitter sentiment at the hospital and patient level as a measure of pediatric patient experience, Open Journal of Pediatrics 11(4) (2021), 706-722.
[16] J. Meyer and S. Okuboyejo, User reviews of depression app features: sentiment analysis, JMIR Formative Research 5(12) (2021), e17062.
[17] P. Bovonratwet, T. S. Shen, W. Islam, M. P. Ast, S. B. Haas and E. P. Su, Natural language processing of patient-experience comments after primary total knee arthroplasty, The Journal of Arthroplasty 36(3) (2021), 927-934.
[18] D. C. Elbers, J. La, J. R. Minot, R. Gramling, M. T. Brophy, N. V. Do, N. R. Fillmore, P. S. Dodds and C. M. Danforth, Sentiment analysis of medical record notes for lung cancer patients at the department of veterans affairs, PLoS One 18(1) (2023), e0280931.
[19] J. N. Cuenca-Zaldívar, M. Torrente-Regidor, L. Martín-Losada, C. Fernández-De- Las-Peñas, L. L. Florencio, P. A. Sousa and D. Palacios-Ceña, Exploring sentiment and care management of hospitalized patients during the first wave of the COVID-19 pandemic using electronic nursing health records: descriptive study, JMIR Medical Informatics 10(5) (2022), e38308.
[20] N. Vandenbussche, C. Van Hee, V. Hoste and K. Paemeleire, Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache, The Journal of Headache and Pain 23(1) (2022), 1-12.
[21] J. Serrano-Guerrero, M. Bani-Doumi, F. P. Romero and J. A. Olivas, Understanding what patients think about hospitals: a deep learning approach for detecting emotions in patient opinions, Artificial Intelligence in Medicine 128 (2022), 102298.
[22] M. Khanbhai, J. Symons, K. Flott, S. Harrison-White, J. Spofforth, R. Klaber and E. Mayer, Enriching the value of patient experience feedback: web-based dashboard development using co-design and heuristic evaluation, JMIR Human Factors 9(1) (2022), e27887.
[23] A. Bittar, S. Velupillai, A. Roberts and R. Dutta, Using general-purpose sentiment lexicons for suicide risk assessment in electronic health records: corpus-based analysis, JMIR Medical Informatics 9(4) (2021), e22397.
[24] D. Spinczyk, M. Bas, M. Dzieciątko, M. Maćkowski, K. Rojewska and S. Maćkowska, Computer-aided therapeutic diagnosis for anorexia, BioMedical Engineering OnLine 19 (2020), 1-23.
[25] R. Huang, N. Liu, M. A. Nicdao, M. Mikaheal, T. Baldacchino, A. Albeos, K. Petoumenos, K. Sud and J. Kim, Emotion sharing in remote patient monitoring of patients with chronic kidney disease, Journal of the American Medical Informatics Association 27(2) (2020), 185-193.
[26] D. Marinello, I. Palla, V. Lorenzoni, G. Andreozzi, S. Pirri, S. Ticciati, S. Cannizzo, A. Del Bianco, E. Ferretti, S. Santoni, G. Turchetti, M. Mosca and R. Talarico, Exploring disease perception in Behçet’s syndrome: combining a quantitative and a qualitative study based on a narrative medicine approach, Orphanet Journal of Rare Diseases 18(1) (2023), 1-15.
[27] S. AlMuhaideb, Y. AlNegheimish, T. AlOmar, R. AlSabti, M. AlKathery and G. AlOlyyan, Analyzing Arabic Twitter-based patient experience sentiments using multi-dialect Arabic bidirectional encoder representations from transformers, Computers, Materials and Continua 76(1) (2023), 195-220.
[28] E. Y. Mohamed, W. Sami, A. Alotaibi, A. Alfarag, A. Almutairi and F. Alanzi, Patients’ satisfaction with primary health care centers’ services, Majmaah, Kingdom of Saudi of Saudi Arabia, International Journal of Health Sciences 9(2) (2015), 163-170.