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Citing this Article

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Published on 23.04.18 in Vol 4, No 2 (2018): Apr-Jun

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

Works citing "Sentiment Analysis of Health Care Tweets: Review of the Methods Used"

According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.5789):

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

  1. Zucco C, Calabrese B, Agapito G, Guzzi PH, Cannataro M. Sentiment analysis for mining texts and social networks data: Methods and tools. WIREs Data Mining and Knowledge Discovery 2020;10(1)
    CrossRef
  2. 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
    CrossRef
  3. Charland-Verville V, Ribeiro de Paula D, Martial C, Cassol H, Antonopoulos G, Chronik BA, Soddu A, Laureys S, Amancio DR. Characterization of near death experiences using text mining analyses: A preliminary study. PLOS ONE 2020;15(1):e0227402
    CrossRef
  4. 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
    CrossRef
  5. Gesualdo F, D’Ambrosio A, Agricola E, Russo L, Campagna I, Ferretti B, Pandolfi E, Cristoforetti M, Tozzi AE, Rizzo C. How do Twitter users react to TV broadcasts dedicated to vaccines in Italy?. European Journal of Public Health 2020;
    CrossRef
  6. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023
    CrossRef
  7. 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
    CrossRef
  8. Lee J, Kim J, Hong YJ, Piao M, Byun A, Song H, Lee HS. Health Information Technology Trends in Social Media: Using Twitter Data. Healthcare Informatics Research 2019;25(2):99
    CrossRef
  9. Johnsen JK, Eggesvik TB, Rørvik TH, Hanssen MW, Wynn R, Kummervold PE. 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
    CrossRef
  10. Liu S, Lee I. Extracting features with medical sentiment lexicon and position encoding for drug reviews. Health Information Science and Systems 2019;7(1)
    CrossRef
  11. 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
    CrossRef
  12. Huddar MG, Sannakki SS, Rajpurohit VS. Multi-level context extraction and attention-based contextual inter-modal fusion for multimodal sentiment analysis and emotion classification. International Journal of Multimedia Information Retrieval 2019;
    CrossRef
  13. Pek JH, Ho VK, Ng WS, 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
    CrossRef
  14. Gomes RF, 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
    CrossRef
  15. 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

According to Crossref, the following books are citing this article (DOI 10.2196/publichealth.5789)

:
  1. Meena R, Bai VT, Omana J. Computational Vision and Bio-Inspired Computing. 2020. Chapter 134:1283
    CrossRef
  2. Liu S, Lee I. Health Information Science. 2018. Chapter 7:75
    CrossRef