Journals / Bitlis Eren University Journal of Science and Technology / 2023 / Cilt: 13 Sayı: 1

Sentiment analysis in social networks of health institutions

Pages
38–60
Publication Date
2023-06-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

However; according to the results of the sentiment analysis, an increase was observed in the “fear” feeling of the institutions during the pandemic period.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Twitter, a communication platform that creates a social impact; it conveys the messages of non-profit organizations to the masses and the emotions of the masses to non-profit organizations.
📊
Key Findings & Data:During the study, a total of 310,341 tweets were collected in three periods, November 2019, May 2020 and October 2020.
💡
Conclusion & Impact:However; according to the results of the sentiment analysis, an increase was observed in the “fear” feeling of the institutions during the pandemic period.
🔬Design:Research Article
📍Scope:2019 Verileri
⚙️Variables:Health · Sentiment Analysis · Text Mining

Abstract

Twitter, a communication platform that creates a social impact; it conveys the messages of non-profit organizations to the masses and the emotions of the masses to non-profit organizations. This research; It aims to examine Twitter posts about health-related non-profit organizations, to determine the emotional states about these institutions on social media and to measure these feelings. Sentiment analysis about WHO, ILO, IOM, UNICEF, FAO, Red Cross, UNDP and UNHCR were carried out using the R program. The tweets used in sentiment analysis were collected by approval of Twitter API. During the study, a total of 310,341 tweets were collected in three periods, November 2019, May 2020 and October 2020. Tweets are classified according to 10 different emotions. One of the main findings of the study is that “positive”, “trust” and “anticipation” feelings are at the top of the tweets shared about these institutions under normal conditions and crisis conditions. Sentiment consistency was tested with Friedman test for each institution after emotional analysis was performed in all institutions (p<0.05). In all institutions except FAO, a significant relationship was found between the emotion medians of the feeling of “fear” according to the periods. However; according to the results of the sentiment analysis, an increase was observed in the “fear” feeling of the institutions during the pandemic period. This research; non-profit international organizations that make environment, health and sustainable development a principle; provides a conceptual framework for understanding when and why follower behavior originates. Thus, it can enable organizations to realize their missions, create meaningful support in times of crisis, increase benefit and awareness, facilitate decision-making, and increase communication and interaction

🔖

Cite This Article

ÇONAK, Ö., ÖNDER, E. (2023). Sentiment analysis in social networks of health institutions. Bitlis Eren University Journal of Science and Technology, Cilt: 13 Sayı: 1, 38–60. https://ajindex.com/articles/951848

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (16)

  1. G. Umbach and C. F. Guidi, “The Importance of Statistics in Public Health Sector Analysis The health policy landscape in the European Union ( EU ): diversity, variety, disparities,” WHO. 2020. Coronavirus Disease 2019 Situation Report - 94. In WHO, vol. 14, 2015
  2. C. Fiarni, H. Maharani and G. R. Wisastra, "Opinion Mining Model System For Indonesian Non Profit Organization Using Multinomial Naive Bayes Algorithm," 2020 8th International Conference on Information and Communication Technology (ICoICT), Yogyakarta, Indonesia, 2020, pp. 1-7, doi: 10.1109/ICoICT49345.2020.9166391
  3. R. Thackeray, B. L. Neiger, S. H. Burton, and C. R. Thackeray, “Analysis of the Purpose of State Health Departments Tweets: Information Sharing, Engagement and Action,” Journal of Medical Internet Research, vol. 15, no. 11, 2013
  4. R. Singh, Major Project Sentiment Analysis of Twitter and Amazon Data using R Programming Master of Business Administration. 2016
  5. Y. Zhao, R and Data Mining: Examples and Case Studies. Elsevier, 2015
  6. A. J. Gentry and D. T. Lang, Package “ROAuth.” https://cran.r-project.org/web/packages/ROAuth/ROAuth.pdf (Accessed: Jun. 29, 2023
  7. E. Neuwirth 2015. Package ‘RColorBrewer’ February. In Cran (Vol. 84, Issue 2)
  8. C. R. Nirmala, G. M. Roopa, and K. R. Kumar, “Twitter Data Analysis for Unemployment Crisis. International Conference on Applied and Theoretical Computing and Communication Technology (ICATccT),” pp. 420–423, 2015
  9. H. Wickham. Package “plyr. R Packages, 82.” https://cran.r-project.org/web/packages/plyr/plyr.pdf (Accessed: Jun. 29, 2023
  10. M. L. Jockers. “Package ‘syuzhet.’ 1–12.” https://cran.rstudio.com/web/packages/syuzhet/syuzhet.pdf (Accessed: Jun. 29, 2023
  11. I. Feinerer. “Introduction to the tm Package Text Mining in R. 02.03.2019.” https://cran.r-project.org/web/packages/tm/vignettes/tm.pdf (Accessed: Jun. 29, 2023
  12. I. Fellows. “Package ‘ wordcloud.” https://cran.r-project.org/web/packages/wordcloud/wordcloud.pdf (Accessed: Jun. 29, 2023
  13. M. Bouchet-Valat. “Package ‘ SnowballC.’ 1–5.” https://r-forge.r-project.org/projects/r-temis/ (Accessed: Jun. 29, 2023
  14. H. Wickham “Package “stringr.” Cran.” https://cran.r-project.org/web/packages/stringr/stringr.pdf (Accessed: Jun. 29, 2023
  15. H. Wickham and C. Winston. 2019. Create Elegant Data Visualisations Using the Grammar of Graphics. Package “Ggplot2,” 3.2.1
  16. I. Sreeja, J. V. Sunny, and L. Jatian, Twitter Sentiment Analysis on Airline T