Published on in Vol 15, No 11 (2013): November

Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online

Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online

Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online

Journals

  1. Xu J, Huang F, Zhang X, Wang S, Li C, Li Z, He Y. Sentiment analysis of social images via hierarchical deep fusion of content and links. Applied Soft Computing 2019;80:387 View
  2. Waudby-Smith I, Tran N, Dubin J, Lee J, van Bogaert P. Sentiment in nursing notes as an indicator of out-of-hospital mortality in intensive care patients. PLOS ONE 2018;13(6):e0198687 View
  3. Choi S, Lee J, Kang M, Min H, Chang Y, Yoon S. Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks. Methods 2017;129:50 View
  4. Schlesinger M, Grob R, Shaller D. Using Patient‐Reported Information to Improve Clinical Practice. Health Services Research 2015;50(S2):2116 View
  5. Contreras I, Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. Journal of Medical Internet Research 2018;20(5):e10775 View
  6. Kim M, Jung Y, Jung D, Hur C. Investigating the Congruence of Crowdsourced Information With Official Government Data: The Case of Pediatric Clinics. Journal of Medical Internet Research 2014;16(2):e29 View
  7. Sanders C, Nahar P, Small N, Hodgson D, Ong B, Dehghan A, Sharp C, Dixon W, Lewis S, Kontopantelis E, Daker-White G, Bower P, Davies L, Kayesh H, Spencer R, McAvoy A, Boaden R, Lovell K, Ainsworth J, Nowakowska M, Shepherd A, Cahoon P, Hopkins R, Allen D, Lewis A, Nenadic G. Digital methods to enhance the usefulness of patient experience data in services for long-term conditions: the DEPEND mixed-methods study. Health Services and Delivery Research 2020;8(28):1 View
  8. McLennan S. The Content and Nature of Narrative Comments on Swiss Physician Rating Websites: Analysis of 849 Comments. Journal of Medical Internet Research 2019;21(9):e14336 View
  9. Gordon D, Ford A, Triedman N, Hart K, Perlis R. Health Care Consumer Shopping Behaviors and Sentiment: Qualitative Study. Journal of Participatory Medicine 2020;12(2):e13924 View
  10. Sedrak M, Salgia M, Decat Bergerot C, Ashing-Giwa K, Cotta B, Adashek J, Dizman N, Wong A, Pal S, Bergerot P. Examining Public Communication About Kidney Cancer on Twitter. JCO Clinical Cancer Informatics 2019;(3):1 View
  11. Mazzocut M, Truccolo I, Antonini M, Rinaldi F, Omero P, Ferrarin E, De Paoli P, Tasso C. Web Conversations About Complementary and Alternative Medicines and Cancer: Content and Sentiment Analysis. Journal of Medical Internet Research 2016;18(6):e120 View
  12. Jung Y, Hur C, Jung D, Kim M. Identifying Key Hospital Service Quality Factors in Online Health Communities. Journal of Medical Internet Research 2015;17(4):e90 View
  13. Park S, Hong S. Identification of Primary Medication Concerns Regarding Thyroid Hormone Replacement Therapy From Online Patient Medication Reviews: Text Mining of Social Network Data. Journal of Medical Internet Research 2018;20(10):e11085 View
  14. Mujtaba G, Shuib L, Idris N, Hoo W, Raj R, Khowaja K, Shaikh K, Nweke H. Clinical text classification research trends: Systematic literature review and open issues. Expert Systems with Applications 2019;116:494 View
  15. Shah A, Yan X, Tariq S, Khan S. Listening to the patient voice: using a sentic computing model to evaluate physicians’ healthcare service quality for strategic planning in hospitals. Quality & Quantity 2021;55(1):173 View
  16. Subirats L, Reguera N, Bañón A, Gómez-Zúñiga B, Minguillón J, Armayones M. Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis. International Journal of Environmental Research and Public Health 2018;15(9):1877 View
  17. Patel S, Cain R, Neailey K, Hooberman L. General Practitioners’ Concerns About Online Patient Feedback: Findings From a Descriptive Exploratory Qualitative Study in England. Journal of Medical Internet Research 2015;17(12):e276 View
  18. Menendez M, Shaker J, Lawler S, Ring D, Jawa A. Negative Patient-Experience Comments After Total Shoulder Arthroplasty. Journal of Bone and Joint Surgery 2019;101(4):330 View
  19. Hao H, Zhang K. The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews. Journal of Medical Internet Research 2016;18(5):e108 View
  20. Taylor C, Al-Hiyari R, Lee S, Priebe A, Guerrero L, Bales A. Beliefs and ideologies linked with approval of corporal punishment: a content analysis of online comments. Health Education Research 2016;31(4):563 View
  21. Tanniru M, Khuntia J. Dimensions of Patient Experience and Overall Satisfaction in Emergency Departments. Journal of Patient Experience 2017;4(3):95 View
  22. Kaur W, Balakrishnan V, Rana O, Sinniah A. Liking, sharing, commenting and reacting on Facebook: User behaviors’ impact on sentiment intensity. Telematics and Informatics 2019;39:25 View
  23. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023 View
  24. Metwally O, Blumberg S, Ladabaum U, Sinha S. Using Social Media to Characterize Public Sentiment Toward Medical Interventions Commonly Used for Cancer Screening: An Observational Study. Journal of Medical Internet Research 2017;19(6):e200 View
  25. Tsoi K, Chan N, Yiu K, Poon S, Lin B, Ho K. Machine Learning Clustering for Blood Pressure Variability Applied to Systolic Blood Pressure Intervention Trial (SPRINT) and the Hong Kong Community Cohort. Hypertension 2020;76(2):569 View
  26. Leung R. Increasing the Impact of JMIR Journals in the Attention Economy. Journal of Medical Internet Research 2019;21(10):e16172 View
  27. Raghupathi V, Zhou Y, Raghupathi W. Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis. International Journal of Healthcare Information Systems and Informatics 2019;14(4):1 View
  28. Bidmon S, Elshiewy O, Terlutter R, Boztug Y. What Patients Value in Physicians: Analyzing Drivers of Patient Satisfaction Using Physician-Rating Website Data. Journal of Medical Internet Research 2020;22(2):e13830 View
  29. Hart K, Perlis R, McCoy T. What do patients learn about psychotropic medications on the web? A natural language processing study. Journal of Affective Disorders 2020;260:366 View
  30. Rafferty M, Grey K. Beyond Patient Experience Surveys: Leveraging Social Media to Glean Patient Feedback. Nurse Leader 2014;12(3):31 View
  31. McCoy T, Castro V, Cagan A, Roberson A, Kohane I, Perlis R, Ramagopalan S. Sentiment Measured in Hospital Discharge Notes Is Associated with Readmission and Mortality Risk: An Electronic Health Record Study. PLOS ONE 2015;10(8):e0136341 View
  32. Hong Y, Liang C, Radcliff T, Wigfall L, Street R. What Do Patients Say About Doctors Online? A Systematic Review of Studies on Patient Online Reviews. Journal of Medical Internet Research 2019;21(4):e12521 View
  33. Kim S, Oh J. Information science techniques for investigating research areas: a case study in telecommunications policy. The Journal of Supercomputing 2018;74(12):6691 View
  34. Piryani R, Madhavi D, Singh V. Analytical mapping of opinion mining and sentiment analysis research during 2000–2015. Information Processing & Management 2017;53(1):122 View
  35. Hu G, Han X, Zhou H, Liu Y. Public Perception on Healthcare Services: Evidence from Social Media Platforms in China. International Journal of Environmental Research and Public Health 2019;16(7):1273 View
  36. Czyżewski A, Hoffmann P, Szczuko P, Kurowski A, Lech M, Szczodrak M. Analysis of results of large‐scale multimodal biometric identity verification experiment. IET Biometrics 2019;8(1):92 View
  37. Emmert M, Meszmer N, Schlesinger M. A cross-sectional study assessing the association between online ratings and clinical quality of care measures for US hospitals: results from an observational study. BMC Health Services Research 2018;18(1) View
  38. Ozan-Rafferty M, Johnson J, Shah G, Kursun A. In the Words of the Medical Tourist: An Analysis of Internet Narratives by Health Travelers to Turkey. Journal of Medical Internet Research 2014;16(2):e43 View
  39. Rastegar-Mojarad M, Ye Z, Wall D, Murali N, Lin S. Collecting and Analyzing Patient Experiences of Health Care From Social Media. JMIR Research Protocols 2015;4(3):e78 View
  40. Matthies B, Coners A. Document Selection for Knowledge Discovery in Texts: Framework Development and Demonstration. Journal of Information & Knowledge Management 2017;16(04):1750038 View
  41. Boylan A, Williams V, Powell J. Online patient feedback: a scoping review and stakeholder consultation to guide health policy. Journal of Health Services Research & Policy 2020;25(2):122 View
  42. Zaman N, Goldberg D, Abrahams A, Essig R. Facebook Hospital Reviews: Automated Service Quality Detection and Relationships with Patient Satisfaction. Decision Sciences 2020 View
  43. Abraham T, Deen T, Hamilton M, True G, O’Neil M, Blanchard J, Uddo M. Analyzing free-text survey responses: An accessible strategy for developing patient-centered programs and program evaluation. Evaluation and Program Planning 2020;78:101733 View
  44. Greaves F, Millett C, Nuki P. England’s Experience Incorporating “Anecdotal” Reports From Consumers into Their National Reporting System. Medical Care Research and Review 2014;71(5_suppl):65S View
  45. Wang X, Parameswaran S, Bagul D, Kishore R. Can online social support be detrimental in stigmatized chronic diseases? A quadratic model of the effects of informational and emotional support on self-care behavior of HIV patients. Journal of the American Medical Informatics Association 2018;25(8):931 View
  46. Chen D, Zhang R, Feng J, Liu K. Fulfilling information needs of patients in online health communities. Health Information & Libraries Journal 2020;37(1):48 View
  47. Gohil S, Vuik S, Darzi A. Sentiment Analysis of Health Care Tweets: Review of the Methods Used. JMIR Public Health and Surveillance 2018;4(2):e43 View
  48. Alemi F. Foreward to special issue on health analytics. Health Care Management Science 2015;18(1):1 View
  49. Urquhart C, Tbaishat D. Reflections on the value and impact of library and information services. Performance Measurement and Metrics 2016;17(1):29 View
  50. Tang C, Zhou L, Plasek J, Rozenblum R, Bates D. Comment Topic Evolution on a Cancer Institution’s Facebook Page. Applied Clinical Informatics 2017;08(03):854 View
  51. Islam M, Hasan M, Wang X, Germack H, Noor-E-Alam M. A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining. Healthcare 2018;6(2):54 View
  52. Hartley B, Elowitz E. Future Directions in Communication in Neurosurgery. World Neurosurgery 2020;133:474 View
  53. Huang M, ElTayeby O, Zolnoori M, Yao L. Public Opinions Toward Diseases: Infodemiological Study on News Media Data. Journal of Medical Internet Research 2018;20(5):e10047 View
  54. Mondal A, Cambria E, Das D, Hussain A, Bandyopadhyay S. Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis. Cognitive Computation 2018;10(4):670 View
  55. Verhoef L, Van de Belt T, Engelen L, Schoonhoven L, Kool R. Social Media and Rating Sites as Tools to Understanding Quality of Care: A Scoping Review. Journal of Medical Internet Research 2014;16(2):e56 View
  56. Patel S, Cain R, Neailey K, Hooberman L. Exploring Patients’ Views Toward Giving Web-Based Feedback and Ratings to General Practitioners in England: A Qualitative Descriptive Study. Journal of Medical Internet Research 2016;18(8):e217 View
  57. Jurdi Z, Crosby J, Harris J, Harvey J. A Closer Examination of the Patient Experience in the Ambulatory Space. Journal of Ambulatory Care Management 2020;43(1):89 View
  58. Xu Z, Guo H. Using Text Mining to Compare Online Pro- and Anti-Vaccine Headlines: Word Usage, Sentiments, and Online Popularity. Communication Studies 2018;69(1):103 View
  59. Zhang W, Deng Z, Hong Z, Evans R, Ma J, Zhang H. Unhappy Patients Are Not Alike: Content Analysis of the Negative Comments from China's Good Doctor Website. Journal of Medical Internet Research 2018;20(1):e35 View
  60. Liu J, Hou S, Evans R, Xia C, Xia W, Ma J. What Do Patients Complain About Online: A Systematic Review and Taxonomy Framework Based on Patient Centeredness. Journal of Medical Internet Research 2019;21(8):e14634 View
  61. Sidey-Gibbons J, Sidey-Gibbons C. Machine learning in medicine: a practical introduction. BMC Medical Research Methodology 2019;19(1) View
  62. Maramba I, Davey A, Elliott M, Roberts M, Roland M, Brown F, Burt J, Boiko O, Campbell J. Web-Based Textual Analysis of Free-Text Patient Experience Comments From a Survey in Primary Care. JMIR Medical Informatics 2015;3(2):e20 View
  63. Gibbons C, Richards S, Valderas J, Campbell J. Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy. Journal of Medical Internet Research 2017;19(3):e65 View
  64. Liu N, Kauffman R. Enhancing healthcare professional and caregiving staff informedness with data analytics for chronic disease management. Information & Management 2021;58(2):103315 View
  65. Wallace B, Paul M, Sarkar U, Trikalinos T, Dredze M. A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews. Journal of the American Medical Informatics Association 2014;21(6):1098 View
  66. Shah A, Yan X, Khan S, Khurrum W, Khan Q. A multi-modal approach to predict the strength of doctor–patient relationships. Multimedia Tools and Applications 2020 View
  67. Lu Y, Wu Y, Liu J, Li J, Zhang P. Understanding Health Care Social Media Use From Different Stakeholder Perspectives: A Content Analysis of an Online Health Community. Journal of Medical Internet Research 2017;19(4):e109 View
  68. KESKİN Ö, AYTEKİN Y. The Evaluation of Financial Participation Banking System in Turkey in the context of Sentiment Analysis. International Journal of Islamic Economics and Finance Studies 2019 View
  69. Li M, Xiang Y, Zhang B, Huang Z. A Sentiment Delivering Estimate Scheme Based on Trust Chain in Mobile Social Network. Mobile Information Systems 2015;2015:1 View
  70. Gebbia V, Piazza D, Valerio M, Borsellino N, Firenze A. Patients With Cancer and COVID-19: A WhatsApp Messenger-Based Survey of Patients’ Queries, Needs, Fears, and Actions Taken. JCO Global Oncology 2020;(6):722 View
  71. Greaves F, Laverty A, Cano D, Moilanen K, Pulman S, Darzi A, Millett C. Tweets about hospital quality: a mixed methods study. BMJ Quality & Safety 2014;23(10):838 View
  72. Patel S, Cain R, Neailey K, Hooberman L. Public Awareness, Usage, and Predictors for the Use of Doctor Rating Websites: Cross-Sectional Study in England. Journal of Medical Internet Research 2018;20(7):e243 View
  73. Nemzer L, Neymotin F. Concierge care and patient reviews. Health Economics 2020;29(8):913 View
  74. Oyebode O, Alqahtani F, Orji R. Using Machine Learning and Thematic Analysis Methods to Evaluate Mental Health Apps Based on User Reviews. IEEE Access 2020;8:111141 View
  75. Shah A, Yan X, Shah S, Mamirkulova G. Mining patient opinion to evaluate the service quality in healthcare: a deep-learning approach. Journal of Ambient Intelligence and Humanized Computing 2020;11(7):2925 View
  76. Wagland R, Recio-Saucedo A, Simon M, Bracher M, Hunt K, Foster C, Downing A, Glaser A, Corner J. Development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer care. BMJ Quality & Safety 2016;25(8):604 View
  77. MacLaren R, Tran V, Chiappe D. Effects of motivation orientation on schoolwork enjoyment and achievement and study habits. Thinking Skills and Creativity 2017;24:199 View
  78. Sewalk K, Tuli G, Hswen Y, Brownstein J, Hawkins J. Using Twitter to Examine Web-Based Patient Experience Sentiments in the United States: Longitudinal Study. Journal of Medical Internet Research 2018;20(10):e10043 View
  79. Emmert M, Meier F, Heider A, Dürr C, Sander U. What do patients say about their physicians? An analysis of 3000 narrative comments posted on a German physician rating website. Health Policy 2014;118(1):66 View
  80. Farrington C, Stewart Z, Barnard K, Hovorka R, Murphy H. Experiences of closed-loop insulin delivery among pregnant women with Type 1 diabetes. Diabetic Medicine 2017;34(10):1461 View
  81. Gabarron E, Larbi D, Dorronzoro E, Hasvold P, Wynn R, Årsand E. Factors Engaging Users of Diabetes Social Media Channels on Facebook, Twitter, and Instagram: Observational Study. Journal of Medical Internet Research 2020;22(9):e21204 View
  82. Jacobs M, Briley P, Ellis C. Quantifying Experiences with Telepractice for Aphasia Therapy: A Text Mining Analysis of Client Response Data. Seminars in Speech and Language 2020;41(05):414 View
  83. Lu Y, Pan T, Liu J, Wu J. Does Usage of Online Social Media Help Users With Depressed Symptoms Improve Their Mental Health? Empirical Evidence From an Online Depression Community. Frontiers in Public Health 2021;8 View
  84. Mathura P, Li M, McMurtry N, Kassam N. Enhancing the healthcare quality improvement storyboard using photovoice. BMJ Open Quality 2020;9(4):e001104 View
  85. Bovonratwet P, Shen T, Islam W, Ast M, Haas S, Su E. Natural Language Processing of Patient-Experience Comments After Primary Total Knee Arthroplasty. The Journal of Arthroplasty 2021;36(3):927 View
  86. Bittar A, Velupillai S, Roberts A, Dutta R. Using General-purpose Sentiment Lexicons for Suicide Risk Assessment in Electronic Health Records: Corpus-Based Analysis. JMIR Medical Informatics 2021;9(4):e22397 View
  87. Sajid A, Awais M, Amir Mehmood M, Batool S, Shahzad A, Zafar A. Patient's Feedback Platform for Quality of Services via “Free Text Analysis” in Healthcare Industry. EMITTER International Journal of Engineering Technology 2020:316 View
  88. Berkovic D, Ackerman I, Briggs A, Ayton D. Tweets by People With Arthritis During the COVID-19 Pandemic: Content and Sentiment Analysis. Journal of Medical Internet Research 2020;22(12):e24550 View
  89. Khanbhai M, Anyadi P, Symons J, Flott K, Darzi A, Mayer E. Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review. BMJ Health & Care Informatics 2021;28(1):e100262 View
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Books/Policy Documents

  1. Altrabsheh N, Kontonatsios G, Korkontzelos Y. Natural Language Processing and Information Systems. View
  2. Lamprinakos G, Aristeidopoulou I, Asanin S, Kapsalis A, Anadiotis A, Kaklamani D, Venieris I. Encyclopedia of E-Health and Telemedicine. View
  3. Abualigah L, Alfar H, Shehab M, Hussein A. Recent Advances in NLP: The Case of Arabic Language. View
  4. Carter P, Kondor K. Digital Extremisms. View
  5. Shah A, Yan X, Shah S, Khan S. Smart Health. View
  6. Xia C, Zhao D, Wang J, Liu J, Ma J. Smart Health. View
  7. Wang K, He C, Wang L, Wu J. Knowledge and Systems Sciences. View
  8. Tang M, Liu Y, Li Z, Liu Y. Intelligent Computing and Internet of Things. View
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  12. Luna-Aveiga H, Medina-Moreira J, Lagos-Ortiz K, Apolinario O, Paredes-Valverde M, del Pilar Salas-Zárate M, Valencia-García R. Advanced Computational Methods for Knowledge Engineering. View
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