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

Infodemiology and Infoveillance (1274) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1570) New Methods (909) Natural Language Processing (644) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1013)

Published on in Vol 17, No 8 (2015): August

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

Authors of this article:

Heather Cole-Lewis1 Author Orcid Image ;   Arun Varghese1 Author Orcid Image ;   Amy Sanders1 Author Orcid Image ;   Mary Schwarz1 Author Orcid Image ;   Jillian Pugatch1 Author Orcid Image ;   Erik Augustson2 Author Orcid Image
Article Authors Cited by (52) Tweetations (48) Metrics

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  19. Leung R. Increasing the Impact of JMIR Journals in the Attention Economy. Journal of Medical Internet Research 2019;21(10):e16172 View
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  22. Lazard A, Wilcox G, Tuttle H, Glowacki E, Pikowski J. Public reactions to e-cigarette regulations on Twitter: a text mining analysis. Tobacco Control 2017;26(e2):e112 View
  23. Zhan Y, Liu R, Li Q, Leischow S, Zeng D. Identifying Topics for E-Cigarette User-Generated Contents: A Case Study From Multiple Social Media Platforms. Journal of Medical Internet Research 2017;19(1):e24 View
  24. Beaunoyer E, Arsenault M, Lomanowska A, Guitton M. Understanding online health information: Evaluation, tools, and strategies. Patient Education and Counseling 2017;100(2):183 View
  25. Kim Y, Kim J. Using photos for public health communication: A computational analysis of the Centers for Disease Control and Prevention Instagram photos and public responses. Health Informatics Journal 2020;26(3):2159 View
  26. Lee S, Liu J, Gibson L, Hornik R. Rating the Valence of Media Content about Electronic Cigarettes Using Crowdsourcing: Testing Rater Instructions and Estimating the Optimal Number of Raters. Health Communication 2021;36(4):497 View
  27. 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
  28. Paul M, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1 View
  29. Mutanga M, Abayomi A. Tweeting on COVID-19 pandemic in South Africa: LDA-based topic modelling approach. African Journal of Science, Technology, Innovation and Development 2022;14(1):163 View
  30. Wang D, Lyu J, Zhao X. Public Opinion About E-Cigarettes on Chinese Social Media: A Combined Study of Text Mining Analysis and Correspondence Analysis. Journal of Medical Internet Research 2020;22(10):e19804 View
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  33. 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
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  35. Xu Q, Yang J, Haupt M, Cai M, Nali M, Mackey T. Digital Surveillance to Identify California Alternative and Emerging Tobacco Industry Policy Influence and Mobilization on Facebook. International Journal of Environmental Research and Public Health 2021;18(21):11150 View
  36. 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
  37. 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
  38. Hassan L, Elkaref M, de Mel G, Bogdanovica I, Nenadic G. Text mining tweets on e-cigarette risks and benefits using machine learning following a vaping related lung injury outbreak in the USA. Healthcare Analytics 2022;2:100066 View
  39. 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
  40. 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
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Books/Policy Documents

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