SPORCU BESLENMESİ İLE İLGİLİ YOUTUBE VİDEO YORUMLARININ DUYGU ANALİZİ

Abstract

Farklı sosyal ağlarda profil oluşturan kullanıcı sayısının hızla artması, bu alanları çeşitli konularda ana veri kaynağı haline getirmiştir. Sağlıklı beslenme ile ilgili sosyal ağlarda yapılan yorumlar genel anlamda bireylerin besin seçimleri ve farkındalıkları hakkındaki varsayımları yansıtsa da insanların sporcu beslenmesi açısından neler tartıştıkları hakkında çok az şey bilinmektedir. Bu çalışmada, sporcu beslenmesiyle ilgili YouTube videolarına ait yorumların duygu içerip içermediği, eğer içeriyorsa bu duygunun olumlu ya da olumsuz olma durumunun metin madenciliği tekniğiyle belirlenmesi gerçekleştirilmiştir. Yapılan analiz sonucunda, sporcu beslenmesi ile ilgili YouTube videolarından elde edilen yorumların %27,62’sinin pozitif, %17,3’ünün negatif, %55,08’inin ise nötr olduğu tespit edilmiştir. Kullanıcıların kreatin ve BCAA (Dallı zincirli amino asit) suplemanlarının tüketimi hakkında olumsuz düşündüğü, karbonhidratlar hakkında nötr; protein kullanımı hakkındaysa hem negatif hem pozitif hem de nötr duygulara sahip oldukları belirlenmiştir.

Keywords

• Duygu Analizi
• Sporcu Beslenmesi
• YouTube Yorumları

SENTIMENT ANALYSIS OF YOUTUBE VIDEOS COMMENTS ON SPORTS NUTRITION

Abstract

The dramatic increase in the number of users creating profiles in different social networks has made these fields the main source of data on various topic. Although the comments made on social networks about healthy eating generally reflect assumptions about individuals' food choices and awareness, little is known about what people are discussing in terms of sports nutrition. The aim of this study is realize YouTube videos about sport nutrition whether contain sentiment or not, and if so whether this sentiment is positive or negative throught text mining technique. Result of analysis, it was determined that 27.62% of the comments obtained from YouTube videos about sport nutrition were positive, 17.3% were negative, and 55.08% were neutral. Additionaly it has been determined that YouTube users had neutral sentiment about carbohydrates, negative sentiment about the use of creatine and BCAA (Branched-chain amino acid) supplements, alongside they had both negative, positive and neutral sentiments about protein use.

Keywords

• Sentiment Analysis
• Sports Nutrition
• YouTube Video Comments

References

1. Albayrak, A. (2020). Doğal Dil İşleme Teknikleri Kullanılarak Disiplinler Arası Lisansüstü Ders İçeriği Hazırlanması. Bilişim Teknolojileri Dergisi, 13(4), 373-383. https://doi.org/10.17671/gazibtd.714447
2. Alaoui, I., Gahi, Y., Messoussi, R. (2019, April). Full Consideration of Big Data Characteristics in Sentiment Analysis Context. 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA 2019), Chengdu, China. https://doi.org/10.1109/ICCCBDA.2019. 8725728
3. Alexa, 2020 https://www.alexa.com/topsites (21.12.2020).
4. Ansari, M. Z., Aziz, M. B., Siddiqui, M. O., Mehra, H., ve Singh, K. P. (2020). Analysis of political sentiment orientations on twitter. Procedia Computer Science, (167), 1821-1828. https://doi.org/10.1016/j.procs.2020.03.201
5. Blackburn, K. G., Yilmaz, G., ve Boyd, R. L. (2018). Food for thought: Exploring how people think and talk about food online. Appetite, 123, 390-401. https://doi.org/10.1016/ j.appet.2018.01.022
6. Bourke, B.E.P., Baker, D.F., Braakhuis, A.J. (2019). Social Media as a Nutrition Resource for Athletes: A Cross Sectional Survey. International Journal of Sport Nutrition and Exercise Metabolism, 29(4), 364-370. https://doi.org/ 10.1123/ijsnem.2018-0135
7. Brown, A., Rambaccussing, D., Reade, J. J., ve Rossi, G. (2018). Forecasting with social media: evidence from tweets on soccer matches. Economic Inquiry, 56(3), 1748-1763. https://doi.org/10.1111/ecin.12506
8. Cambria, E., Schuller, B., Xia, Y., ve Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent systems, 28(2), 15-21. https://doi.org/ 10.1109/MIS.2013.30

9. Catalani,V., Negri, A., Townshend, H., Simonato,P., Prilutskaya,M., Tippett,A., Corazza,O.(2021). The market of sport supplement in the digital era: A netnographic analysis of perceived risks, side-effects and other safety issues. Emerging Trends in Drugs, Addictions, and Health, 1 (100014),1-8. https://doi.org/10.1016/j.etdah.2021. 100014
10. Chawai, A.I.B. (2019). Türkçe Metinlerde Sözlük Tabanlı Yaklaşımla Duygu Analizi ve Görselleştirme, [Yüksek Lisans tezi, Marmara Üniversitesi]. https://avesis.marmara.edu.tr/yonetilen-tez/7d083cb9-2d47-44f3-8e36-5a770faaaa42/turkce-metinlerde-sozluk-tabanli-yaklasimla-duygu-analizi-ve-gorsellestirme
11. Chelaru, S., Orellana-Rodriguez, C., ve Altingovde, I.S. (2013). “How useful is social feedback for learning to rank YouTube videos?”. In World Wide Web, 17(5),1-29. https://doi.org/10.1007/s11280-013-0258-9
12. Congeni, J., ve Miller, S. (2002). Supplements and drugs used to enhance athletic performance. Pediatric Clinics of North America, 49(2), 435-461. https://doi.org/10.1016/ S0031-3955(01)00013-X
13. Demšar, J., Zupan, B., Leban, G., ve Curk, T. (2004, September). Orange: From experimental machine learning to interactive data mining. In European conference on principles of data mining and knowledge discovery (pp. 537-539). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30116-5_58
14. Dunne, D. M., Lefevre, C., Cunniffe, B., Tod, D., Close, G. L., Morton, J. P., ve Murphy, R. (2019). Performance Nutrition in the digital era–An exploratory study into the use of social media by sports nutritionists. Journal of sports sciences, 37(21), 2467-2474. https://doi.org/10.1080/ 02640414.2019.1642052
15. Gabarron, E., Dorronzoro, E., Rivera-Romero, O., ve Wynn, R. (2019). Diabetes on Twitter: A Sentiment Analysis. Journal of Diabetes Science and Technology, 13(3), 439-444. https://doi.org/10.1177%2F1932296818811679
16. Gill, P., Arlitt, M., Li, Z., ve Mahanti, A. (2007, October). Youtube traffic characterization: A view from the edge. Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement (IMC07), 2007, San Diego California, USA. https://doi.org/10.1145/1298306.1298310
17. Horsburgh, H., ve Barron, D. (2019). Who are the experts?: Examining the online promotion of misleading and harmful nutrition information. In Medical Misinformation and Social Harm in Non-Science-Based Health Practices (pp. 100-115). Routledge.
18. Isah, H., Trundle, P., ve Neagu, D. (2014, September). Social media analysis for product safety using text mining and sentiment analysis. In 2014 14th UK workshop on computational intelligence (UKCI) (pp. 1-7). IEEE. https://doi.org/10.1109/UKCI.2014.6930158
19. Kasaba, E., ve Yıldıztepe, E. (2016). Destek Vektör Makinesi Yöntemi ile Bir Duygu Çözümlemesi. Akademik Bilişim Yayınları.
20. Kaynar, O., Görmez, Y., Yildiz, M., Albayrak, A. (2016, Eylül). Makine Öğrenmesi Yöntemleri ile Duygu Analizi-Sentiment Analysis with Machine Learning Techniques. International Artificial Intelligence and Data Processing Symposium (IDAP'16), 2016, Malatya, Türkiye.
21. Kulshrestha, J. (2016, February). Measuring and managing information diets of social media users: Research overview. Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion (CSCW '16), 2016, San Francisco, USA. https://doi.org/10.1145/2818052.2874354
22. Lee, S. H., Lee, J. Y., ve Kim, H. H. (2018). Online Reputation Analysis of Dietary Supplements based on Sentiment Analysis. In Proceedings of the Korea Information Processing Society Conference (pp. 306-308). Korea Information Processing Society. https://doi.org/10.3745/PKIPS.y2018m05a.306
23. Li, J., Lowe, D., Wayment, L., ve Huang, Q. (2020). Text mining datasets of β-hydroxybutyrate (BHB) supplement products’ consumer online reviews. Data in brief, 30, 105385. https://doi.org/10.1016/j.dib.2020.105385
24. Ljajić, A., Ljajić, E., Spalević, P., Arsić, B., ve Vučković, D. (2015, September). Sentiment analysis of textual comments in field of sport. In 24nd International Electrotechnical and Computer Science Conference (ERK 2015), IEEE, Slovenia.
25. Mahan, L., ve Raymond, J. (2017). Food & The Nutrition Care Process. Canada: Elsevier Press.
26. Maharana, A., Cai, K., Hellerstein, J., Hswen, Y., Munsell, M., Staneva, V., ... ve Nsoesie, E. O. (2019). Detecting reports of unsafe foods in consumer product reviews. JAMIA open, 2(3), 330-338. https://doi.org/10.1093/jamiaopen /ooz030
27. Masih, J., Verbeke, W., Deutsch, J., Sharma, A., Sharma, A., Rajkumar, R., ve Matharu, P. S. (2019). Big Data Study for Gluten-Free Foods in India and USA Using Online Reviews and Social Media. Agricultural Sciences, 10(3), 302-320. http://dx.doi.org/10.4236/as.2019.103026
28. Medhat, W., Hassan, A., ve Korashy, H. (2014). Sentiment Analysis Algorithms and Applications: A Survey. Ain Shams Engineering Journal, 5 (4), 1093-1113. https://doi.org/10.1016/j.asej.2014.04.011
29. McAuley, J., ve Leskovec, J. (2013). Hidden factors and hidden topics: Understanding rating dimensions with review text. Proceedings of the 7th ACM Conference on Recommender Systems (pp. 165–172). New York, NY: ACM. https://doi.org/10.1145/2507157.2507163
30. Nadobnik, J. (2018). The Use of Selected Social Media: Instagram to Promote Physical Activity and a Pro-Health Lifestyle. Studies In Sport Humanities (24),31-38. http://dx.doi.org/10.5604/01.3001.0013.7563
31. Nikfarjam, A., Sarker, A., O'Connor, K., Ginn, R., ve Gonzalez, G. (2015). Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical Informatics Association, 22(3), 671–681. https://doi.org/10.1093/jamia/ocu041
32. Pai, R.R., ve Alathur, S. (2018). Assessing mobile health applications with twitter analytics. International Journal of Medical Informatics (113), 72-84. https://doi.org/10.1016/j.ijmedinf.2018.02.016
33. Pantazopoulos, A., Maragoudakis, M. (2018, July). Sports & Nutrition Data Science using Gradient Boosting Machines. 10th Hellenic Conference on Artificial Intelligence (SETN '18), 2018, Patras, Greece. https://doi.org/10.1145/3200947.3201060
34. Pimenta, F., Lopes, L., Gonçalves, F., Campos, P. (2020, November). Designing Positive Behavior Change Experiences: a Systematic Review and Sentiment Analysis based on Online User Reviews of Fitness and Nutrition Mobile Applications. 19th International Conference on Mobile and Ubiquitous Multimedia (MUM 2020), 2020, Essen, Germany. https://doi.org/10.1145/3428361. 3428403
35. Piryani, R., Madhavi, D., ve Singh, V. K. (2017). Analytical mapping of opinion mining and sentiment analysis research during 2000–2015. Information Processing & Management, 53(1), 122-150. https://doi.org/10.1016/j.ipm.2016.07.001
36. Procter, R., Vis, F., ve Voss A. (2013). Reading the riots on Twitter: methodological innovation for the analysis of big data. International Journal of Social Research Methodology, 16 (3),197-214. https://doi.org/10.1080/13645579. 2013.774172
37. Rajput, S., ve Sharma, P. (2021). Virtual Gazing, Unhealthy Vlogs and Food Choices: A Behavioural Analysis. International Journal Of Multidisciplinary Educational Research, 10:4(2),154-164.
38. Sarıman, G., ve Mutaf, E. Covıd-19 Sürecinde Twıtter Mesajlarının Duygu Analizi. Euroasia Journal of Mathematics, Engineering, Natural & Medical Sciences International Indexed & Refereed, 7(10),137-148. http://dx.doi.org/10.38065/euroasiaorg.149
39. Saura, J.R., Reyes-Menendez, A., ve Thomas, S.B. (2020). Gaining a deeper understanding of nutrition using social networks and user-generated content. Internet Interventions, 20 (100312),1-19. https://doi.org/10.1016/j.invent.2020. 100312
40. Schumaker, R. P., Jarmoszko, A. T., ve Labedz Jr, C. S. (2016). Predicting wins and spread in the Premier League using a sentiment analysis of twitter. Decision Support Systems, (88), 76-84. https://doi.org/10.1016/j.dss.2016.05.010
41. Severyn, A., Moschitti, A., Uryupina, O., Plank, B., Filippova, K. (2016). Multi-lingual opinion mining on YouTube. Information Processing & Management, 52:(1),46-60. https://doi.org/10.1016/j.ipm.2015.03.002
42. Shafaee, A., Issa, H., Agne, S., Baumann, S., ve Dengel, A. (2014, May). Aspect-based sentiment analysis of amazon reviews for fitness tracking devices. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 50-61). Springer, Cham. 10.1007/978-3-319-13186-3_6.
43. Shaw, G. (2018). Characterizing Health Behavior Information: Developing A Surveillance Text Mining Framework Using Twitter For Diet, Diabetes, Exercise, And Obesity, [Doctoral Dissertation, University of South Carolina]. https://scholarcommons.sc.edu/etd/4889/
44. Salas-Zárate, M. D., Medina-Moreira, J., Lagos-Ortiz, K., Luna-Aveiga, H., Rodríguez-García, M. Á., ve Valencia-García, R. (2017). Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach. Computational and Mathematical Methods in Medicine, (2017), 1-9. https://doi.org/10.1155/2017/5140631
45. Strand, M., ve Gustafsson, S.A. (2020). Mukbang and Disordered Eating: A Netnographic Analysis of Online Eating Broadcasts. Culture, Medicine and Psychiatry, (44),586-609. https://doi.org/10.1007/s11013-020-09674-6
46. Stirling, E., Willcox, J., Ong, K. L., ve Forsyth, A. (2021). Social media analytics in nutrition research: a rapid review of current usage in investigation of dietary behaviours. Public Health Nutrition, 24(6),1193-1209. https://doi.org/10.1017/s1368980020005248
47. Sullivan, R., Sarker, A., O'Connor, K., Goodin, A., Karlsrud, M., ve Gonzalez, G. (2016, January). Finding Potentially Unsafe Nutritional Supplements From User Reviews With Topic Modeling. Pacific Symposium on Biocomputing (PSB 2016),2016, Hawaii, USA. https://doi.org/10.1142/ 9789814749411_0048
48. Tao, D., Yang, P., ve Feng, H. (2020). Utilization of text mining as a big data analysis tool for food science and nutrition. Comprehensive reviews in food science and food safety, 19(2), 875-894. https://doi.org/10.1111/1541-4337.12540
49. Teng, S., Khong, K.W., Sharif, S.P., Ahmed, A. (2020). YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis. JMIR Public Health And Surveillance 6 (4), 1-13. https://doi.org/10.2196/19618
50. Vidal, L., Ares, G., Machín, L., ve Jaeger, S. R. (2015). Using Twitter data for food-related consumer research: A case study on “what people say when tweeting about different eating situations”. Food Quality and Preference, (45), 58-69. https://doi.org/10.1016/j.foodqual.2015.05.006
51. Wang, Y., McKee, M., Torbica, A., ve Stuckler, D. (2019). Systematic literature review on the spread of health-related misinformation on social media. Social science & medicine, (240), 112552. https://doi.org/10.1016/ j.socscimed.2019.112552
52. Wunderlich, F., ve Memmert, D. (2020). Innovative approaches in sports science lexicon-based sentiment analysis as a tool to analyze sports-related Twitter communication. Applied Sciences, 10(2), 431. https://doi.org/10.3390/ app10020431