Abstract
The objectives of this research were to analyze the relationship of the phrases or words commonly found in the comments from depression hashtag on Twitter using the association rules. The data used in this study were collected from comments in Thai language via depression hashtag on Twitter during 1 January 2019 to 31 January 2019, in total of 1,500 comments. According to the comments in Thai language on social media collected by using Rapidminer Studio 9 software to get the word about depression and used to analyze relationships of words from a text comment to get the format data Association. The frequency of words and phrases in a form of presentation is used to describe the various opinions about the depression that has a presentation on social media. According to the model performance in each of the above methods, it was found that Euclidean Distance provided the best result due to the smallest average distance at all points in each cluster which was equal to 152.504. The association analysis, a total of 30 association rules were obtained, the support of 0.5% and the 80% confidence.
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References
Spector, N., Kappel, D.M.: Guidelines for Using Electronic and Social Media: The Regulatory Perspective. http://www.nursingworld.org/MainMenuCategories/ANAMarketplace/ANAPeriodicals/OJIN/TableofContents/Vol-17-2012/No3-Sept-2012/Guidelines-for-Electronic-and-Social-Media.html
Zocial. Thai social network that marketers should not miss. http://www.zocialinc.com/blog-social-network. (in Thai)
Thawisuwan, P.: Twitter Sukhoi, 1st edn. Success Media, Bangkok (2009). (in Thai)
ETDA Thailand, Internet user’s behavior in 2018, Thai people use the internet for 10 hours, 5 minutes per a day. https://www.etda.or.th/content/etda-reveals-thailand-internet-user-profile-2018.html. (in Thai)
Kumar, A., Sharma, A., Arora, A.: Anxious depression prediction in real-time social data. In: Proceedings of International Conference on Advanced Engineering, Science, Management and Technology (ICAESMT 2019) (2019)
Eichstaedt, J.C., Smith, R.J., Merchant, R.M., Ungar, L.H., Crutchley, P., Preoţiuc-Pietro, D., Schwartz, H.A.: Facebook language predicts depression in medical records. Proc. Natl. Acad. Sci. U.S.A. 115(44), 11203–11208 (2018)
De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (2013)
Charoenpornsawat, P.: Feature-based Thai word segmentation. Master of Engineering in Computer Engineering thesis, Chulalongkorn University (1998). (in Thai)
Tongaree, P.: A comparative study of word segmentation on Thai text categorization. Master of Computer Science thesis, Thammasat University (2008). (in Thai)
Rygielski, C., Wang, J.-C., Yen, D.C.: Data mining techniques for customer relationship management. Technol. Soc. 24(23), 483–502 (2002)
Thepmak, S.: The market basket analysis system using association rules case study cosmetic. Master of Science thesis, Information Technology Faculty, King Mongkut’s University of Technology North Bangkok (2011). (in Thai)
National Institute of Mental Health (NIH), Depression. https://www.nimh.nih.gov/health/topics/depression/index.shtml
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Chaiwuttisak, P. (2021). Text Mining Analysis of Comments in Thai Language for Depression from Online Social Networks. In: Kreinovich, V., Hoang Phuong, N. (eds) Soft Computing for Biomedical Applications and Related Topics. Studies in Computational Intelligence, vol 899. Springer, Cham. https://doi.org/10.1007/978-3-030-49536-7_26
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