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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1010)

Published on in Vol 4, No 2 (2018): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/5789, first published .
Sentiment Analysis of Health Care Tweets: Review of the Methods Used

Sentiment Analysis of Health Care Tweets: Review of the Methods Used

Sentiment Analysis of Health Care Tweets: Review of the Methods Used

Authors of this article:

Sunir Gohil1 Author Orcid Image ;   Sabine Vuik1 Author Orcid Image ;   Ara Darzi1 Author Orcid Image
Article Authors Cited by (106) Tweetations (8) Metrics

Journals

  1. Gomes R, Casais B. Feelings generated by threat appeals in social marketing: text and emoji analysis of user reactions to anorexia nervosa campaigns in social media. International Review on Public and Nonprofit Marketing 2018;15(4):591 View
  2. Zucco C, Calabrese B, Agapito G, Guzzi P, Cannataro M. Sentiment analysis for mining texts and social networks data: Methods and tools. WIREs Data Mining and Knowledge Discovery 2020;10(1) View
  3. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  4. Hu T, She B, Duan L, Yue H, Clunis J. A Systematic Spatial and Temporal Sentiment Analysis on Geo-Tweets. IEEE Access 2020;8:8658 View
  5. Charland-Verville V, Ribeiro de Paula D, Martial C, Cassol H, Antonopoulos G, Chronik B, Soddu A, Laureys S, Amancio D. Characterization of near death experiences using text mining analyses: A preliminary study. PLOS ONE 2020;15(1):e0227402 View
  6. Feldhege J, Moessner M, Bauer S. Who says what? Content and participation characteristics in an online depression community. Journal of Affective Disorders 2020;263:521 View
  7. Lee J, Kim J, Hong Y, Piao M, Byun A, Song H, Lee H. Health Information Technology Trends in Social Media: Using Twitter Data. Healthcare Informatics Research 2019;25(2):99 View
  8. Gesualdo F, D’Ambrosio A, Agricola E, Russo L, Campagna I, Ferretti B, Pandolfi E, Cristoforetti M, Tozzi A, Rizzo C. How do Twitter users react to TV broadcasts dedicated to vaccines in Italy?. European Journal of Public Health 2020;30(3):481 View
  9. Pilozzi A, Huang X. Overcoming Alzheimer’s Disease Stigma by Leveraging Artificial Intelligence and Blockchain Technologies. Brain Sciences 2020;10(3):183 View
  10. Alotaibi S, Mehmood R, Katib I, Rana O, Albeshri A. Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning. Applied Sciences 2020;10(4):1398 View
  11. Johnsen J, Eggesvik T, Rørvik T, Hanssen M, Wynn R, Kummervold P. Differences in Emotional and Pain-Related Language in Tweets About Dentists and Medical Doctors: Text Analysis of Twitter Content. JMIR Public Health and Surveillance 2019;5(1):e10432 View
  12. Liu S, Lee I. Extracting features with medical sentiment lexicon and position encoding for drug reviews. Health Information Science and Systems 2019;7(1) View
  13. Garcia-Rudolph A, Laxe S, Saurí J, Bernabeu Guitart M. Stroke Survivors on Twitter: Sentiment and Topic Analysis From a Gender Perspective. Journal of Medical Internet Research 2019;21(8):e14077 View
  14. 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
  15. Huddar M, Sannakki S, Rajpurohit V. Multi-level context extraction and attention-based contextual inter-modal fusion for multimodal sentiment analysis and emotion classification. International Journal of Multimedia Information Retrieval 2020;9(2):103 View
  16. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023 View
  17. Guo M, Ganz O, Cruse B, Navarro M, Wagner D, Tate B, Delahanty J, Benoza G. Keeping It Fresh With Hip-Hop Teens: Promising Targeting Strategies for Delivering Public Health Messages to Hard-to-Reach Audiences. Health Promotion Practice 2020;21(1_suppl):61S View
  18. Toloza F, Espinoza Suarez N, El Kawkgi O, Golembiewski E, Ponce O, Yao L, Maraka S, Singh Ospina N, Brito J. Patient Experiences and Perceptions Associated with the Use of Desiccated Thyroid Extract. Medicina 2020;56(4):161 View
  19. Pek J, Ho V, Ng W, Kabir T, Tiah L, Koh Y. Missed opportunities for organ donation in patients with intracranial haemorrhage at the emergency department: A single-centre study. Proceedings of Singapore Healthcare 2019;28(4):274 View
  20. OLIVEIRA L, ZANATTA F. Self-reported dental treatment needs during the COVID-19 outbreak in Brazil: an infodemiological study. Brazilian Oral Research 2020;34 View
  21. Petersen C, Halter R, Kotz D, Loeb L, Cook S, Pidgeon D, Christensen B, Batsis J. Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study. JMIR mHealth and uHealth 2020;8(8):e16862 View
  22. Bansal A, Padappayil R, Garg C, Singal A, Gupta M, Klein A. Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review. Journal of Medical Systems 2020;44(9) View
  23. Al-Rawi A, Siddiqi M, Morgan R, Vandan N, Smith J, Wenham C. COVID-19 and the Gendered Use of Emojis on Twitter: Infodemiology Study. Journal of Medical Internet Research 2020;22(11):e21646 View
  24. Shapiro L, Thomas K, Eppler S, Behal R, Yao J, Kamal R. Understanding the Patient Experience: Analysis of 2-Word Assessment and Its Relationship to Likelihood to Recommend in Outpatient Hand Surgery. HAND 2022;17(6):1201 View
  25. 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
  26. Castillo-Sánchez G, Marques G, Dorronzoro E, Rivera-Romero O, Franco-Martín M, De la Torre-Díez I. Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review. Journal of Medical Systems 2020;44(12) View
  27. He L, Yin T, Hu Z, Chen Y, Hanauer D, Zheng K. Developing a standardized protocol for computational sentiment analysis research using health-related social media data. Journal of the American Medical Informatics Association 2021;28(6):1125 View
  28. Spinazze P, Aardoom J, Chavannes N, Kasteleyn M. The Computer Will See You Now: Overcoming Barriers to Adoption of Computer-Assisted History Taking (CAHT) in Primary Care. Journal of Medical Internet Research 2021;23(2):e19306 View
  29. Stirling E, Willcox J, Ong K, Forsyth A. Social media analytics in nutrition research: a rapid review of current usage in investigation of dietary behaviours. Public Health Nutrition 2021;24(6):1193 View
  30. Pavan Kumar C, Dhinesh Babu L. Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks. Journal of Intelligent & Fuzzy Systems 2021;40(6):11749 View
  31. Huddar M, Sannakki S, Rajpurohit V. Attention-based multimodal contextual fusion for sentiment and emotion classification using bidirectional LSTM. Multimedia Tools and Applications 2021;80(9):13059 View
  32. Guarita B, Belackova V, van der Gouwe D, Blankers M, Pazitny M, Griffiths P. Monitoring drug trends in the digital environment–New methods, challenges and the opportunities provided by automated approaches. International Journal of Drug Policy 2021;94:103210 View
  33. Zhu Y, Cao L, Xie J, Yu Y, Chen A, Huang F. Using social media data to assess the impact of COVID-19 on mental health in China. Psychological Medicine 2021:1 View
  34. Khemasuwan D, Colt H. Applications and challenges of AI-based algorithms in the COVID-19 pandemic. BMJ Innovations 2021;7(2):387 View
  35. Margus C, Brown N, Hertelendy A, Safferman M, Hart A, Ciottone G. Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study. Journal of Medical Internet Research 2021;23(7):e28615 View
  36. Shi N, Zhang D, Li L, Xu S, Wang H. Predicting Mental Health Problems with Automatic Identification of Metaphors. Journal of Healthcare Engineering 2021;2021:1 View
  37. Polisena J, Andellini M, Salerno P, Borsci S, Pecchia L, Iadanza E. Case Studies on the Use of Sentiment Analysis to Assess the Effectiveness and Safety of Health Technologies: A Scoping Review. IEEE Access 2021;9:66043 View
  38. Batra R, Imran A, Kastrati Z, Ghafoor A, Daudpota S, Shaikh S. Evaluating Polarity Trend Amidst the Coronavirus Crisis in Peoples’ Attitudes toward the Vaccination Drive. Sustainability 2021;13(10):5344 View
  39. Shofiya C, Abidi S. Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data. International Journal of Environmental Research and Public Health 2021;18(11):5993 View
  40. Yang Y, Al-Garadi M, Love J, Perrone J, Sarker A. Automatic gender detection in Twitter profiles for health-related cohort studies. JAMIA Open 2021;4(2) View
  41. ELGOHARY E, ABD-ELAZIZ M. Data mining framework for analyzing Twitter users' opinion on the drug mefloquine. Gazzetta Medica Italiana Archivio per le Scienze Mediche 2021;180(5) View
  42. MacKay M, Colangeli T, Gillis D, McWhirter J, Papadopoulos A. Examining Social Media Crisis Communication during Early COVID-19 from Public Health and News Media for Quality, Content, and Corresponding Public Sentiment. International Journal of Environmental Research and Public Health 2021;18(15):7986 View
  43. Zhang X, Mu L, Zhang D, Mao Y, Shi L, Rajbhandari-Thapa J, Chen Z, Li Y, Pagán J. Geographical and Temporal Analysis of Tweets Related to COVID-19 and Cardiovascular Disease in the US. Annals of GIS 2022;28(4):491 View
  44. Arnould A, Hendricusdottir R, Bergmann J. The Complexity of Medical Device Regulations Has Increased, as Assessed through Data-Driven Techniques. Prosthesis 2021;3(4):314 View
  45. Rahim A, Ibrahim M, Chua S, Musa K. Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier. Healthcare 2021;9(12):1679 View
  46. Awoyemi T, Ogunniyi K, Adejumo A, Ebili U, Olusanya A, Olojakpoke E, Shonibare O. Emotional Analysis of Tweets About Clinically Extremely Vulnerable COVID-19 Groups. Cureus 2022 View
  47. Vaishali P, Kumari P. Ensemble learning based classifier to predict depression caused due to pandemic. Journal of Physics: Conference Series 2021;2089(1):012026 View
  48. A. Rahim A, Ibrahim M, Musa K, Chua S, Yaacob N. Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews. International Journal of Environmental Research and Public Health 2021;18(18):9912 View
  49. Baker W, Colditz J, Dobbs P, Mai H, Visweswaran S, Zhan J, Primack B. Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study. JMIR Medical Informatics 2022;10(7):e33678 View
  50. Sukhera J, Ahmed H. Leveraging Machine Learning to Understand How Emotions Influence Equity Related Education: Quasi-Experimental Study. JMIR Medical Education 2022;8(1):e33934 View
  51. Alshaabi T, Van Oort C, Fudolig M, Arnold M, Danforth C, Dodds P. Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning. Frontiers in Artificial Intelligence 2022;4 View
  52. Ng J, Abdelkader W, Lokker C. Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data. BMC Complementary Medicine and Therapies 2022;22(1) View
  53. Gao C, Espinoza Suarez N, Toloza F, Malaga Zuniga A, McCarthy S, Boehmer K, Yao L, Fu S, Brito J. Patients’ Perspective About the Cost of Diabetes Management: An Analysis of Online Health Communities. Mayo Clinic Proceedings: Innovations, Quality & Outcomes 2021;5(5):898 View
  54. Menaouer B, Zahra A, Mohammed S. Multi-Class Sentiment Classification for Healthcare Tweets Using Supervised Learning Techniques. International Journal of Service Science, Management, Engineering, and Technology 2022;13(1):1 View
  55. Rahim A, Ibrahim M, Musa K, Chua S, Yaacob N. Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook. Healthcare 2021;9(10):1369 View
  56. Obiedat R, Al-Qaisi L, Qaddoura R, Harfoushi O, Al-Zoubi A. An Intelligent Hybrid Sentiment Analyzer for Personal Protective Medical Equipments Based on Word Embedding Technique: The COVID-19 Era. Symmetry 2021;13(12):2287 View
  57. Stupinski A, Alshaabi T, Arnold M, Adams J, Minot J, Price M, Dodds P, Danforth C. Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study. JMIR Mental Health 2022;9(3):e33685 View
  58. Yu H, Yang C, Yu P, Liu K, Patel S. Emotion diffusion effect: Negative sentiment COVID-19 tweets of public organizations attract more responses from followers. PLOS ONE 2022;17(3):e0264794 View
  59. Mathieson S, O'Keeffe M, Traeger A, Ferreira G, Abdel Shaheed C. Content and sentiment analysis of gabapentinoid‐related tweets: An infodemiology study. Drug and Alcohol Review 2024;43(1):45 View
  60. Bhatia S, Jason L. Using Data Mining and Time Series to Investigate ME and CFS Naming Preferences. Journal of Disability Policy Studies 2024;35(1):65 View
  61. Boukobza A, Burgun A, Roudier B, Tsopra R. Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set. JMIR Medical Informatics 2022;10(5):e34306 View
  62. Walsh J, Dwumfour C, Cave J, Griffiths F. Spontaneously generated online patient experience data - how and why is it being used in health research: an umbrella scoping review. BMC Medical Research Methodology 2022;22(1) View
  63. Arias F, Zambrano Nunez M, Guerra-Adames A, Tejedor-Flores N, Vargas-Lombardo M. Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics. IEEE Access 2022;10:74850 View
  64. Brezulianu A, Burlacu A, Popa I, Arif M, Geman O. “Not by Our Feeling, But by Other's Seeing”: Sentiment Analysis Technique in Cardiology—An Exploratory Review. Frontiers in Public Health 2022;10 View
  65. Ainley E, Witwicki C, Tallett A, Graham C. Using Twitter Comments to Understand People’s Experiences of UK Health Care During the COVID-19 Pandemic: Thematic and Sentiment Analysis. Journal of Medical Internet Research 2021;23(10):e31101 View
  66. Lee I, Juang S, Chen S, Ko C, Ma K. Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience. Frontiers in Medicine 2022;9 View
  67. Yashpal S, Raghunath A, Gencerliler N, Burns L. Exploring Public Perceptions of Dental Care Affordability in the United States: Mixed Method Analysis via Twitter. JMIR Formative Research 2022;6(7):e36315 View
  68. Imran M, Qazi U, Ofli F. TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels. Data 2022;7(1):8 View
  69. Newman J, Fink J, Clough L, Johnston S. Internal Medicine Clerkship ID Curriculum Flip: Will They Prefer to Pre-learn?. Medical Science Educator 2021;31(6):1751 View
  70. Zou Y, Wang J, Lei Z, Zhang Y, Wang W, Hasikin K. Sentiment Analysis for Necessary Preview of 30-Day Mortality in Sepsis Patients and the Control Strategies. Journal of Healthcare Engineering 2021;2021:1 View
  71. Elkaim L, Levett J, Niazi F, Alvi M, Shlobin N, Linzey J, Robertson F, Bokhari R, Alotaibi N, Lasry O. Cervical Myelopathy and Social Media: Mixed Methods Analysis. Journal of Medical Internet Research 2023;25:e42097 View
  72. Molenaar A, Jenkins E, Brennan L, Lukose D, McCaffrey T. The use of sentiment and emotion analysis and data science to assess the language of nutrition-, food- and cooking-related content on social media: a systematic scoping review. Nutrition Research Reviews 2024;37(1):43 View
  73. Flores L, Kim S, Young S. Addressing bias in artificial intelligence for public health surveillance. Journal of Medical Ethics 2024;50(3):190 View
  74. Bennett E, Topp S, Moodie A. National Public Health Surveillance of Corporations in Key Unhealthy Commodity Industries – A Scoping Review and Framework Synthesis. International Journal of Health Policy and Management 2023;12:6876 View
  75. Alexander F, Tucker R, Jones B, Hendricks S. X as a proxy for tackle safety culture? Sentiment analysis of social media posts on red-carded and yellow-carded tackles during the 2019 Rugby World Cup. BMJ Open Sport & Exercise Medicine 2023;9(4):e001756 View
  76. Zargaran D, Zargaran A, Sousi S, Knight D, Cook H, Woollard A, Davies J, Weyrich T, Mosahebi A. Quantitative and qualitative analysis of individual experiences post botulinum toxin injection ‐ United Kingdom Survey. Skin Health and Disease 2023;3(5) View
  77. Meksawasdichai S, Lerksuthirat T, Ongphiphadhanakul B, Sriphrapradang C. Perspectives and Experiences of Patients With Thyroid Cancer at a Global Level: Retrospective Descriptive Study of Twitter Data. JMIR Cancer 2023;9:e48786 View
  78. Chen J, Creamer G, Ning Y, Ben-Zvi T. Healthcare Sustainability: Hospitalization Rate Forecasting with Transfer Learning and Location-Aware News Analysis. Sustainability 2023;15(22):15840 View
  79. Lossio-Ventura J, Weger R, Lee A, Guinee E, Chung J, Atlas L, Linos E, Pereira F. A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data. JMIR Mental Health 2024;11:e50150 View
  80. Hasan M, Wiedyaningsih C, Yasin N. Public Perspective on Hyperlipidemia Drugs and Sentiments About Hyperlipidemia on Twitter. Borneo Journal of Pharmacy 2023;6(3):330 View
  81. Wang Z, He S, Xu G, Ren M. Will sentiment analysis need subculture? A new data augmentation approach. Journal of the Association for Information Science and Technology 2024;75(6):655 View
  82. A. Semary N, Ahmed W, Amin K, Pławiak P, Hammad M, Sarwar N. Enhancing machine learning-based sentiment analysis through feature extraction techniques. PLOS ONE 2024;19(2):e0294968 View
  83. Molenaar A, Lukose D, Brennan L, Jenkins E, McCaffrey T. Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study. Journal of Medical Internet Research 2024;26:e47826 View
  84. Sousa‐Pinto B, Jankin S, Vieira R, Marques‐Cruz M, Fonseca J, Bousquet J. English tweets on allergy: Content analysis and association with surveillance data. Clinical & Experimental Allergy 2024 View
  85. Fernandez-Perez A, Indriawan I, Khomyn M. Emotions and Stock Returns during the GameStop Bubble. SSRN Electronic Journal 2024 View
  86. Souaad Hamza-Cherif , Lamia Fatiha Kazi Tani , Nesma Settouti . Improving Healthcare Communication: AI-Driven Emotion Classification in Imbalanced Patient Text Data with Explainable Models. Advances in Technology Innovation 2024;9(2):129 View
  87. Dcosta J, Graul A, Hasnat S. Understanding Consumer Adoption of Light-Duty Electric Vehicles: An Interdisciplinary Literature Review. Transportation Research Record: Journal of the Transportation Research Board 2024 View

Books/Policy Documents

  1. Liu S, Lee I. Health Information Science. View
  2. Meena R, Bai V, Omana J. Computational Vision and Bio-Inspired Computing. View
  3. Malik M, Naaz S. Computer Networks, Big Data and IoT. View
  4. Patil A, Jain K, Mohapatra S, Singh S. Cognitive Intelligence and Big Data in Healthcare. View
  5. Varun P, Manohar G, Kumar T, Pavan Kumar C. Intelligent Data Engineering and Analytics. View
  6. Bharambe U, Ingle P, Ramesh R, Mahato M. Intelligent Solutions for Cognitive Disorders. View