Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 01.11.13 in Vol 15, No 11 (2013): November

This paper is in the following e-collection/theme issue:

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

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.2721):

(note that this is only a small subset of citations)

  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
    CrossRef
  2. Waudby-Smith IER, Tran N, Dubin JA, 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
    CrossRef
  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
    CrossRef
  4. Schlesinger M, Grob R, Shaller D. Using Patient-Reported Information to Improve Clinical Practice. Health Services Research 2015;50:2116
    CrossRef
  5. Contreras I, Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. Journal of Medical Internet Research 2018;20(5):e10775
    CrossRef
  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
    CrossRef
  7. Sanders C, Nahar P, Small N, Hodgson D, Ong BN, Dehghan A, Sharp CA, Dixon WG, 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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  10. Sedrak MS, Salgia MM, Decat Bergerot C, Ashing-Giwa K, Cotta BN, Adashek JJ, Dizman N, Wong AR, Pal SK, Bergerot PG. Examining Public Communication About Kidney Cancer on Twitter. JCO Clinical Cancer Informatics 2019;(3):1
    CrossRef
  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
    CrossRef
  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
    CrossRef
  13. Park SH, Hong SH. 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
    CrossRef
  14. Mujtaba G, Shuib L, Idris N, Hoo WL, Raj RG, Khowaja K, Shaikh K, Nweke HF. Clinical text classification research trends: Systematic literature review and open issues. Expert Systems with Applications 2019;116:494
    CrossRef
  15. Shah AM, 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 2020;
    CrossRef
  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
    CrossRef
  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
    CrossRef
  18. Menendez ME, Shaker J, Lawler SM, Ring D, Jawa A. Negative Patient-Experience Comments After Total Shoulder Arthroplasty. The Journal of Bone and Joint Surgery 2019;101(4):330
    CrossRef
  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
    CrossRef
  20. Taylor CA, Al-Hiyari R, Lee SJ, Priebe A, Guerrero LW, Bales A. Beliefs and ideologies linked with approval of corporal punishment: a content analysis of online comments. Health Education Research 2016;31(4):563
    CrossRef
  21. Tanniru M, Khuntia J. Dimensions of Patient Experience and Overall Satisfaction in Emergency Departments. Journal of Patient Experience 2017;4(3):95
    CrossRef
  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
    CrossRef
  23. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023
    CrossRef
  24. Metwally O, Blumberg S, Ladabaum U, Sinha SR. 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
    CrossRef
  25. Tsoi KK, Chan NB, Yiu KK, Poon SK, 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
    CrossRef
  26. Leung R. Increasing the Impact of JMIR Journals in the Attention Economy. Journal of Medical Internet Research 2019;21(10):e16172
    CrossRef
  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
    CrossRef
  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
    CrossRef
  29. Hart KL, Perlis RH, McCoy TH. What do patients learn about psychotropic medications on the web? A natural language processing study. Journal of Affective Disorders 2020;260:366
    CrossRef
  30. Rafferty MO, Grey K. Beyond Patient Experience Surveys: Leveraging Social Media to Glean Patient Feedback. Nurse Leader 2014;12(3):31
    CrossRef
  31. McCoy TH, Castro VM, Cagan A, Roberson AM, Kohane IS, Perlis RH, Ramagopalan SV. Sentiment Measured in Hospital Discharge Notes Is Associated with Readmission and Mortality Risk: An Electronic Health Record Study. PLOS ONE 2015;10(8):e0136341
    CrossRef
  32. Hong YA, Liang C, Radcliff TA, Wigfall LT, Street RL. 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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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)
    CrossRef
  38. Ozan-Rafferty ME, Johnson JA, Shah GH, 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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  42. Zaman N, Goldberg DM, Abrahams AS, Essig RA. Facebook Hospital Reviews: Automated Service Quality Detection and Relationships with Patient Satisfaction. Decision Sciences 2020;
    CrossRef
  43. Abraham TH, Deen TL, Hamilton M, True G, O’Neil MT, 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
    CrossRef
  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
    CrossRef
  45. Wang X, Parameswaran S, Bagul DM, 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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  48. Alemi F. Foreward to special issue on health analytics. Health Care Management Science 2015;18(1):1
    CrossRef
  49. Urquhart C, Tbaishat D. Reflections on the value and impact of library and information services. Performance Measurement and Metrics 2016;17(1):29
    CrossRef
  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
    CrossRef
  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
    CrossRef
  52. Hartley BR, Elowitz E. Future Directions in Communication in Neurosurgery. World Neurosurgery 2020;133:474
    CrossRef
  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
    CrossRef
  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
    CrossRef
  55. Verhoef LM, Van de Belt TH, Engelen LJ, Schoonhoven L, Kool RB. Social Media and Rating Sites as Tools to Understanding Quality of Care: A Scoping Review. Journal of Medical Internet Research 2014;16(2):e56
    CrossRef
  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
    CrossRef
  57. Jurdi ZR, Crosby JF, Harris JE, Harvey JB. A Closer Examination of the Patient Experience in the Ambulatory Space. Journal of Ambulatory Care Management 2020;43(1):89
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  61. Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Medical Research Methodology 2019;19(1)
    CrossRef
  62. Maramba ID, Davey A, Elliott MN, 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
    CrossRef
  63. Gibbons C, Richards S, Valderas JM, 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
    CrossRef
  64. Liu N, Kauffman RJ. Enhancing healthcare professional and caregiving staff informedness with data analytics for chronic disease management. Information & Management 2020;:103315
    CrossRef
  65. Wallace BC, Paul MJ, Sarkar U, Trikalinos TA, 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
    CrossRef
  66. Shah AM, Yan X, Khan S, Khurrum W, Khan QR. A multi-modal approach to predict the strength of doctor–patient relationships. Multimedia Tools and Applications 2020;
    CrossRef
  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
    CrossRef
  68. KESKİN , AYTEKİN YE. The Evaluation of Financial Participation Banking System in Turkey in the context of Sentiment Analysis. International Journal of Islamic Economics and Finance Studies 2019;
    CrossRef
  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
    CrossRef
  70. Gebbia V, Piazza D, Valerio MR, 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
    CrossRef
  71. Greaves F, Laverty AA, Cano DR, Moilanen K, Pulman S, Darzi A, Millett C. Tweets about hospital quality: a mixed methods study. BMJ Quality & Safety 2014;23(10):838
    CrossRef
  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
    CrossRef
  73. Nemzer LR, Neymotin F. Concierge care and patient reviews. Health Economics 2020;29(8):913
    CrossRef
  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
    CrossRef
  75. Shah AM, Yan X, Shah SAA, 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
    CrossRef
  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
    CrossRef
  77. MacLaren R, Tran VH, Chiappe D. Effects of motivation orientation on schoolwork enjoyment and achievement and study habits. Thinking Skills and Creativity 2017;24:199
    CrossRef
  78. Sewalk KC, Tuli G, Hswen Y, Brownstein JS, Hawkins JB. Using Twitter to Examine Web-Based Patient Experience Sentiments in the United States: Longitudinal Study. Journal of Medical Internet Research 2018;20(10):e10043
    CrossRef
  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
    CrossRef
  80. Farrington C, Stewart ZA, Barnard K, Hovorka R, Murphy HR. Experiences of closed-loop insulin delivery among pregnant women with Type 1 diabetes. Diabetic Medicine 2017;34(10):1461
    CrossRef
  81. Gabarron E, Larbi D, Dorronzoro E, Hasvold PE, 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
    CrossRef
  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;
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/jmir.2721):

  1. Altrabsheh N, Kontonatsios G, Korkontzelos Y. Natural Language Processing and Information Systems. 2019. Chapter 23:286
    CrossRef
  2. Lamprinakos G, Aristeidopoulou IA, Asanin S, Kapsalis AP, Anadiotis AG, Kaklamani DI, Venieris IS. Encyclopedia of E-Health and Telemedicine. 2016. chapter 68:889
    CrossRef
  3. Abualigah L, Alfar HE, Shehab M, Hussein AMA. Recent Advances in NLP: The Case of Arabic Language. 2020. Chapter 7:129
    CrossRef
  4. Carter P, Kondor K. Digital Extremisms. 2020. Chapter 11:223
    CrossRef
  5. Shah AM, Yan X, Shah SJ, Khan S. Smart Health. 2018. Chapter 19:191
    CrossRef
  6. Xia C, Zhao D, Wang J, Liu J, Ma J. Smart Health. 2018. Chapter 23:231
    CrossRef
  7. Wang K, He C, Wang L, Wu J. Knowledge and Systems Sciences. 2018. Chapter 4:45
    CrossRef
  8. Tang M, Liu Y, Li Z, Liu Y. Intelligent Computing and Internet of Things. 2018. Chapter 10:99
    CrossRef
  9. Bernabé-Moreno J, Tejeda-Lorente A, Porcel C, Herrera-Viedma E. Advances in Fuzzy Logic and Technology 2017. 2018. Chapter 19:199
    CrossRef
  10. Parimbelli E, Quaglini S, Bellazzi R, Holmes JH. Artificial Intelligence in Medicine. 2015. Chapter 13:106
    CrossRef
  11. Canbolat ZN, Pinarbasi F. Exploring the Power of Electronic Word-of-Mouth in the Services Industry. 2020. chapter 7:101
    CrossRef
  12. Luna-Aveiga H, Medina-Moreira J, Lagos-Ortiz K, Apolinario O, Paredes-Valverde MA, del Pilar Salas-Zárate M, Valencia-García R. Advanced Computational Methods for Knowledge Engineering. 2018. Chapter 13:141
    CrossRef
  13. Rathi M, Jain N, Bist P, Agrawal T. High Performance Vision Intelligence. 2020. Chapter 17:245
    CrossRef