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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1050) Infodemiology and Infoveillance (1369) Pharmacovigilance (138)

Published on in Vol 2, No 2 (2016): Jul-Dec

“When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets

“When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets

“When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets

Authors of this article:

Raminta Daniulaityte1, 2 Author Orcid Image ;   Lu Chen2 Author Orcid Image ;   Francois R Lamy1, 2 Author Orcid Image ;   Robert G Carlson1, 2 Author Orcid Image ;   Krishnaprasad Thirunarayan2 Author Orcid Image ;   Amit Sheth2 Author Orcid Image
Article Authors Cited by (50) Tweetations (11) Metrics

Journals

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  6. 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
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  15. Lamy F, Daniulaityte R, Zatreh M, Nahhas R, Sheth A, Martins S, Boyer E, Carlson R. "You got to love rosin: Solventless dabs, pure, clean, natural medicine." Exploring Twitter data on emerging trends in Rosin Tech marijuana concentrates. Drug and Alcohol Dependence 2018;183:248 View
  16. Kim S, Marsch L, Hancock J, Das A. Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data. Journal of Medical Internet Research 2017;19(10):e353 View
  17. Daniulaityte R, Lamy F, Barratt M, Nahhas R, Martins S, Boyer E, Sheth A, Carlson R. Characterizing marijuana concentrate users: A web-based survey. Drug and Alcohol Dependence 2017;178:399 View
  18. 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
  19. Singh T, Roberts K, Cohen T, Cobb N, Wang J, Fujimoto K, Myneni S. Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review. JMIR Public Health and Surveillance 2020;6(4):e21660 View
  20. 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
  21. Oyebode O, Lomotey R, Orji R. “I Tried to Breastfeed but…”: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic Analysis. IEEE Access 2021;9:61074 View
  22. Tsai M, Wang Y. Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19. International Journal of Environmental Research and Public Health 2021;18(12):6272 View
  23. 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
  24. Najafizada M, Rahman A, Donnan J, Dong Z, Bishop L. Analyzing sentiments and themes on cannabis in Canada using 2018 to 2020 Twitter data. Journal of Cannabis Research 2022;4(1) View
  25. Scaboro S, Portelli B, Chersoni E, Santus E, Serra G. Increasing adverse drug events extraction robustness on social media: Case study on negation and speculation. Experimental Biology and Medicine 2022;247(22):2003 View
  26. Black J, Margolin Z, Bau G, Olson R, Iwanicki J, Dart R. Web-Based Discussion and Illicit Street Sales of Tapentadol and Oxycodone in Australia: Epidemiological Surveillance Study. JMIR Public Health and Surveillance 2021;7(12):e29187 View
  27. Lokala U, Lamy F, Daniulaityte R, Gaur M, Gyrard A, Thirunarayan K, Kursuncu U, Sheth A. Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study. JMIR Public Health and Surveillance 2022;8(12):e24938 View
  28. 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
  29. 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
  30. 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
  31. 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
  32. Khademi Habibabadi S, Hallinan C, Bonomo Y, Conway M. Consumer-Generated Discourse on Cannabis as a Medicine: Scoping Review of Techniques. Journal of Medical Internet Research 2022;24(11):e35974 View
  33. Tang J, Arvind V, Dominy C, White C, Cho S, Kim J. How Are Patients Reviewing Spine Surgeons Online? A Sentiment Analysis of Physician Review Website Written Comments. Global Spine Journal 2023;13(8):2107 View
  34. He L, Yin T, Zheng K. They May Not Work! An evaluation of eleven sentiment analysis tools on seven social media datasets. Journal of Biomedical Informatics 2022;132:104142 View
  35. 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
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  38. SV P, Gajjar P. Critique of the paper, ‘Investigating the attitude and perspectives of Indian citizens toward COVID-19 vaccines: A text analytics study’. International Journal of Disaster Risk Reduction 2024;100:104104 View
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Books/Policy Documents

  1. Natsiavas P, Maglaveras N, Koutkias V. Knowledge Representation for Health Care. View
  2. Kursuncu U, Gaur M, Lokala U, Thirunarayan K, Sheth A, Arpinar I. Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining. View
  3. Zhang H, Wheldon C, Tao C, Dunn A, Guo Y, Huo J, Bian J. Social Web and Health Research. View
  4. Leightley D, Sharp M, Williamson V, Fear N, Gribble R. Social Media and the Armed Forces. View
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  6. Leightley D, Sharp M, Williamson V, Fear N, Gribble R. Soziale Medien und die Streitkräfte. View