Detecting associations between dietary supplement intake and sentiments within mental disorder tweets

Yefeng Wang, Yunpeng Zhao, Jianqiu Zhang, Jiang Bian, Rui Zhang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Many patients with mental disorders take dietary supplement, but their use patterns remain unclear. In this study, we developed a method to detect signals of associations between dietary supplement intake and mental disorder in Twitter data. We developed an annotated dataset and trained a convolutional neural network classifier that can identify language use pattern of dietary supplement intake with an F1-score of 0.899, a precision of 0.900, and a recall of 0.900. Using the classifier, we discovered that melatonin and vitamin D were the most commonly used supplements among Twitter users who self-diagnosed mental disorders. Sentiment analysis using Linguistic Inquiry and Word Count has shown that among Twitter users who posted mental disorder self-diagnosis, users who indicated supplement intake are more active and express more negative emotions and fewer positive emotions than those who have not mentioned supplement intake.

Original languageEnglish (US)
Pages (from-to)803-815
Number of pages13
JournalHealth Informatics Journal
Volume26
Issue number2
DOIs
StatePublished - Jun 1 2020

Bibliographical note

Funding Information:
https://orcid.org/0000-0003-2153-8187 Wang Yefeng University of Minnesota, USA https://orcid.org/0000-0002-5771-3373 Zhao Yunpeng University of Florida, USA Zhang Jianqiu University of Minnesota, USA https://orcid.org/0000-0002-2238-5429 Bian Jiang University of Florida, USA Zhang Rui University of Minnesota, USA Rui Zhang, Institute for Health Informatics and College of Pharmacy, University of Minnesota, 8-100 Phillips-Wangensteen Building, 516 Delaware Street SE, Minneapolis, MN 55455, USA. Email: zhan1386@umn.edu 9 2019 1460458219867231 © The Author(s) 2019 2019 SAGE Publications This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( http://www.creativecommons.org/licenses/by-nc/4.0/ ) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage ). Many patients with mental disorders take dietary supplement, but their use patterns remain unclear. In this study, we developed a method to detect signals of associations between dietary supplement intake and mental disorder in Twitter data. We developed an annotated dataset and trained a convolutional neural network classifier that can identify language use pattern of dietary supplement intake with an F1-score of 0.899, a precision of 0.900, and a recall of 0.900. Using the classifier, we discovered that melatonin and vitamin D were the most commonly used supplements among Twitter users who self-diagnosed mental disorders. Sentiment analysis using Linguistic Inquiry and Word Count has shown that among Twitter users who posted mental disorder self-diagnosis, users who indicated supplement intake are more active and express more negative emotions and fewer positive emotions than those who have not mentioned supplement intake. dietary supplement mental health natural language processing sentiment analysis social media NSF Award 1734134 National Center for Complementary & Integrative Health of the National Institutes of Health R01AT009457 edited-state corrected-proof The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or National Science Foundation. Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was supported by the National Center for Complementary & Integrative Health and the Office of Dietary Supplements (ODS) of the National Institutes of Health under Award Number R01AT009457 (PI: Zhang) and NSF Award #1734134 (PI: Bian). ORCID iDs Yefeng Wang https://orcid.org/0000-0003-2153-8187 Yunpeng Zhao https://orcid.org/0000-0002-5771-3373 Jiang Bian https://orcid.org/0000-0002-2238-5429

Funding Information:
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or National Science Foundation. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was supported by the National Center for Complementary & Integrative Health and the Office of Dietary Supplements (ODS) of the National Institutes of Health under Award Number R01AT009457 (PI: Zhang) and NSF Award #1734134 (PI: Bian).

Publisher Copyright:
© The Author(s) 2019.

Keywords

  • dietary supplement
  • mental health
  • natural language processing
  • sentiment analysis
  • social media

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

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