Araştırma Makalesi
BibTex RIS Kaynak Göster

Sentiment analysis in social networks of health institutions

Yıl 2023, Cilt: 13 Sayı: 1, 38 - 60, 30.06.2023

Özge Çonak Emrah Önder

https://doi.org/10.17678/beuscitech.1222933

Öz

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

Anahtar Kelimeler

Health Sentiment Analysis Text Mining Nonprofit Organization Twitter.

Kaynakça

  • [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 Tweets in India Using R Language. Third National Conference on Computational Intelligence (NCCI 2019). 2020.
  • [17] J. Gentry. “Package twitteR.” https://cran.r-project.org/web/packages/twitteR/twitteR.pdf (Accessed: Jun. 29, 2023)
  • [18] H. Kaya, V. Alcan, M. Zinnuroğlu, G. K. Karataş, and S. Çoban, “Analysis of free text in electronic health records by using text mining methods,” in 7th International Conference on Advanced Technologies (ICAT’18), Antalya, Turkey, 2018.
  • [19] A. M. Alayba, V. Palade, M. England, and R. Iqbal, “Arabic language sentiment analysis on health services,” in 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR), 2017.
  • [20] M. S. Neethu and R. Rajasree, “Sentiment analysis in twitter using machine learning techniques,” in 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), 2013.
  • [21] K. Lovejoy and G. D. Saxton, “Information , Community , and Action,” How Nonprofit Organizations Use Social Media, vol. 17, pp. 337–353, 2012.
  • [22] A. Chung, H. Woo, and K. Lee, “Understanding the information diffusion of tweets of a non-profit organization that targets female audiences : an examination of Women Who Code ’ s tweets,” Journal of Communication Management, 2020.
  • [23] H. Park, B. H. Reber, and M. Chon, “Tweeting as Health Communication : Health Organizations Use of Twitter for Health Promotion and Public Engagement,” Journal of Health Communication, vol. 0, pp. 1–11, 2016.
  • [24] M. Aydemir and H. B. Akyol, “#imnotavirus: Pro-Migrant Activism on Twitter amidst the Global Corona Virus (Covid-19) Outbreak,” Social Sciences & Humanities Open, 2020.
  • [25] K. Kristofferson, K. White, and J. Peloza, “The Nature of Slacktivism : How the Social Observability of an Initial Act of Token Prosocial Action,” Journal of Consumer Research, 2014.
  • [26] R. B. Hubert, E. Estevez, A. Maguitman, and T. Janowski, “Examining government-citizen interactions on Twitter using visual and sentiment analysis,” in Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, 2018.
  • [27] A. Reyes-Menendez, J. R. Saura, and C. Alvarez-Alonso, “Understanding #WorldEnvironmentDay user opinions in Twitter: A topic-based sentiment analysis approach,” Int. J. Environ. Res. Public Health, vol. 15, no. 11, p. 2537, 2018.

Yıl 2023, Cilt: 13 Sayı: 1, 38 - 60, 30.06.2023

Özge Çonak Emrah Önder

https://doi.org/10.17678/beuscitech.1222933

Öz

Anahtar Kelimeler

Health Sentiment Analysis Text Mining Nonprofit Organization Twitter.

Kaynakça

  • [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 Tweets in India Using R Language. Third National Conference on Computational Intelligence (NCCI 2019). 2020.
  • [17] J. Gentry. “Package twitteR.” https://cran.r-project.org/web/packages/twitteR/twitteR.pdf (Accessed: Jun. 29, 2023)
  • [18] H. Kaya, V. Alcan, M. Zinnuroğlu, G. K. Karataş, and S. Çoban, “Analysis of free text in electronic health records by using text mining methods,” in 7th International Conference on Advanced Technologies (ICAT’18), Antalya, Turkey, 2018.
  • [19] A. M. Alayba, V. Palade, M. England, and R. Iqbal, “Arabic language sentiment analysis on health services,” in 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR), 2017.
  • [20] M. S. Neethu and R. Rajasree, “Sentiment analysis in twitter using machine learning techniques,” in 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), 2013.
  • [21] K. Lovejoy and G. D. Saxton, “Information , Community , and Action,” How Nonprofit Organizations Use Social Media, vol. 17, pp. 337–353, 2012.
  • [22] A. Chung, H. Woo, and K. Lee, “Understanding the information diffusion of tweets of a non-profit organization that targets female audiences : an examination of Women Who Code ’ s tweets,” Journal of Communication Management, 2020.
  • [23] H. Park, B. H. Reber, and M. Chon, “Tweeting as Health Communication : Health Organizations Use of Twitter for Health Promotion and Public Engagement,” Journal of Health Communication, vol. 0, pp. 1–11, 2016.
  • [24] M. Aydemir and H. B. Akyol, “#imnotavirus: Pro-Migrant Activism on Twitter amidst the Global Corona Virus (Covid-19) Outbreak,” Social Sciences & Humanities Open, 2020.
  • [25] K. Kristofferson, K. White, and J. Peloza, “The Nature of Slacktivism : How the Social Observability of an Initial Act of Token Prosocial Action,” Journal of Consumer Research, 2014.
  • [26] R. B. Hubert, E. Estevez, A. Maguitman, and T. Janowski, “Examining government-citizen interactions on Twitter using visual and sentiment analysis,” in Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, 2018.
  • [27] A. Reyes-Menendez, J. R. Saura, and C. Alvarez-Alonso, “Understanding #WorldEnvironmentDay user opinions in Twitter: A topic-based sentiment analysis approach,” Int. J. Environ. Res. Public Health, vol. 15, no. 11, p. 2537, 2018.
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makalesi
Yazarlar

Özge Çonak 0000-0001-5381-4022

Emrah Önder 0000-0002-0554-1290

Yayımlanma Tarihi 30 Haziran 2023
Gönderilme Tarihi 22 Aralık 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 13 Sayı: 1

Kaynak Göster

IEEE Ö. Çonak ve E. Önder, “Sentiment analysis in social networks of health institutions”, Bitlis Eren University Journal of Science and Technology, c. 13, sy. 1, ss. 38–60, 2023, doi: 10.17678/beuscitech.1222933.
 Kapak Resmi İndir