Exploring the Evolving Perception of Sports Doping through Text Analytics: Figuring out Public Sentiments
Abstract
This study employs text analytics to investigate the evolving public perception of sports doping, utilizing natural language processing techniques such as sentiment analysis and Latent Dirichlet Allocation (LDA) topic modeling. The data, comprising 64,725 English tweets collected from January 2018 to December 2022 using the snscrape Python library, underwent refinement through NLP algorithms, including the removal of stop words, punctuation, digits, and hyperlinks. Stemming and lemmatization techniques were applied for text uniformity and structure enhancement. The sentiment analysis revealed dynamic shifts in neutral, pessimistic, and optimistic sentiments across different years, indicating changing public attitudes toward sports doping. Visual representations through figures and tables enhance comprehension of the sentiment distribution trends. LDA topic modeling identified critical themes in the sports doping discourse, encompassing anti-doping regulations, financial impacts on sports, marijuana use, women's participation, dope screening procedures, ethical considerations, anti-doping organizations, specific doping instances, and consequences of doping bans. The research illuminates a nuanced understanding of the intricate issue by presenting these findings visually. The study highlights a notable shift in public attitudes towards sports doping. It underscores the dynamic nature of opinions, emphasizing the importance of continuous observation and comprehension of factors influencing these fluctuations. The LDA analysis provides valuable insights into various dimensions of the sports doping debate, contributing to a more comprehensive understanding of this complex and evolving issue.
References
- Abeza, G., & Sanderson, J. (2022). Theory and social media in sport studies. International Journal of Sport Communication, 15(4), 284–292.
- Barget, E., & Chavinier-Rela, S. (2017). The analysis of amateur sports clubs funding: A European perspective. Athens Journal of Sports, 4(1), 7–34.
- Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134.
- Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.
- Collomp, K., Ericsson, M., Bernier, N., & Buisson, C. (2022). Prevalence of prohibited substance use and methods by female athletes: Evidence of gender-related differences. Frontiers in Sports and Active Living, 4, 839976.
- Davis, P., & Ryall, E. (2017). Evaluating violent conduct in sport: A hierarchy of vice. Sport, Ethics and Philosophy, 11(2), 207–218.
- De Hon, O., Kuipers, H., & Van Bottenburg, M. (2015). Prevalence of doping use in elite sports: A review of numbers and methods. Sports Medicine, 45, 57–69.
- Dougherty, J. W., & Baron, D. (2022). Substance Use and Addiction in Athletes: The Case for Neuromodulation and Beyond. International Journal of Environmental Research and Public Health, 19(23), 16082. https://doi.org/10.3390/ijerph192316082
- Farkhod, A., Abdusalomov, A., Makhmudov, F., & Cho, Y. I. (2021). LDA-based topic modeling sentiment analysis using topic/document/sentence (TDS) model. Applied Sciences, 11(23), 11091.
- García-Grimau, E., De la Vega, R., De Arce, R., & Casado, A. (2021). Attitudes toward and susceptibility to doping in Spanish elite and national-standard track and field athletes: An examination of the Sport Drug Control Model. Frontiers in Psychology, 12, 679001.
- Heuberger, J. A., Henning, A., Cohen, A. F., & Kayser, B. (2022). Dealing with doping. A plea for better science, governance and education. British Journal of Clinical Pharmacology, 88(2), 566–578.
- Nanda, G., Jaiswal, A., Castellanos, H., Zhou, Y., Choi, A., & Magana, A. J. (2023). Evaluating the Coverage and Depth of Latent Dirichlet Allocation Topic Model in Comparison with Human Coding of Qualitative Data: The Case of Education Research. Machine Learning and Knowledge Extraction, 5(2), 473–490.
- Olympian Sharron Davies reveals death threats during trans debate. (2022). https://nypost.com/2022/06/21/olympian-sharron-davies-reveals-death-threats-during-trans-debate/
- Panja, T. (2019). Russia banned from Olympics and global sports for 4 years over doping. The New York Times, 9.
- Perishable. (2019, May 1). Effects of Performance-Enhancing Drugs | USADA. https://www.usada.org/athletes/substances/effects-of-performance-enhancing-drugs/
- Pöppel, K. (2021). Efficient ways to combat doping in a sports education context!? A systematic review on doping prevention measures focusing on young age groups. Frontiers in Sports and Active Living, 3, 673452.
- Praveen, S., Ittamalla, R., & Deepak, G. (2021). Analyzing the attitude of Indian citizens towards COVID-19 vaccine–A text analytics study. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 15(2), 595–599.
- Reardon, C. L., & Creado, S. (2014). Drug abuse in athletes. Substance Abuse and Rehabilitation, 5, 95–105. https://doi.org/10.2147/SAR.S53784
- Sefiha, O., & Reichman, N. (2017). Social media and the doping of sport surveillance. Sociology Compass, 11(10), e12509.
- Singh, M., Jakhar, A. K., & Pandey, S. (2021). Sentiment analysis on the impact of coronavirus in social life using the BERT model. Social Network Analysis and Mining, 11(1), 33.
- Subramanian, D. (2021, August 10). Scrape Tweets using snscrape and Build a Sentiment Classifier. Medium. https://pub.towardsai.net/scraping-tweets-using-snscrape-and-building-sentiment-classifier-13811dadd11d
- SV, P., & Ittamalla, R. (2022). General public’s attitude toward governments implementing digital contact tracing to curb COVID-19–a study based on natural language processing. International Journal of Pervasive Computing and Communications, 18(5), 485–490.
- Uyar, Y., Gentile, A., Uyar, H., Erdeveciler, Ö., Sunay, H., Mîndrescu, V., Mujkic, D., & Bianco, A. (2022). Competition, gender equality, and doping in sports in the red queen effect perspective. Sustainability, 14(5), 2490.
- Vernikou, S., Lyras, A., & Kanavos, A. (2022). Multiclass sentiment analysis on COVID-19-related tweets using deep learning models. Neural Computing and Applications, 34(22), 19615–19627.
- Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731–5780.
- Xu, Q. (2023). Competing as the First Out Transgender Female Olympian: A Twitter Network Analysis of Laurel Hubbard During the 2020 Tokyo Games. Communication & Sport, 11(5), 854–878. https://doi.org/10.1177/21674795221090422
Exploring the Evolving Perception of Sports Doping through Text Analytics: Figuring out Public Sentiments
Abstract
This study employs text analytics to investigate the evolving public perception of sports doping, utilizing natural language processing techniques such as sentiment analysis and Latent Dirichlet Allocation (LDA) topic modeling. The data, comprising 64,725 English tweets collected from January 2018 to December 2022 using the snscrape Python library, underwent refinement through NLP algorithms, including the removal of stop words, punctuation, digits, and hyperlinks. Stemming and lemmatization techniques were applied for text uniformity and structure enhancement. The sentiment analysis revealed dynamic shifts in neutral, pessimistic, and optimistic sentiments across different years, indicating changing public attitudes toward sports doping. Visual representations through figures and tables enhance comprehension of the sentiment distribution trends. LDA topic modeling identified critical themes in the sports doping discourse, encompassing anti-doping regulations, financial impacts on sports, marijuana use, women's participation, dope screening procedures, ethical considerations, anti-doping organizations, specific doping instances, and consequences of doping bans. The research illuminates a nuanced understanding of the intricate issue by presenting these findings visually. The study highlights a notable shift in public attitudes towards sports doping. It underscores the dynamic nature of opinions, emphasizing the importance of continuous observation and comprehension of factors influencing these fluctuations. The LDA analysis provides valuable insights into various dimensions of the sports doping debate, contributing to a more comprehensive understanding of this complex and evolving issue.
Keywords
Sports doping sentiment analysis text analytics topic modeling Latent Dirichlet Allocation.
References
- Abeza, G., & Sanderson, J. (2022). Theory and social media in sport studies. International Journal of Sport Communication, 15(4), 284–292.
- Barget, E., & Chavinier-Rela, S. (2017). The analysis of amateur sports clubs funding: A European perspective. Athens Journal of Sports, 4(1), 7–34.
- Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134.
- Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.
- Collomp, K., Ericsson, M., Bernier, N., & Buisson, C. (2022). Prevalence of prohibited substance use and methods by female athletes: Evidence of gender-related differences. Frontiers in Sports and Active Living, 4, 839976.
- Davis, P., & Ryall, E. (2017). Evaluating violent conduct in sport: A hierarchy of vice. Sport, Ethics and Philosophy, 11(2), 207–218.
- De Hon, O., Kuipers, H., & Van Bottenburg, M. (2015). Prevalence of doping use in elite sports: A review of numbers and methods. Sports Medicine, 45, 57–69.
- Dougherty, J. W., & Baron, D. (2022). Substance Use and Addiction in Athletes: The Case for Neuromodulation and Beyond. International Journal of Environmental Research and Public Health, 19(23), 16082. https://doi.org/10.3390/ijerph192316082
- Farkhod, A., Abdusalomov, A., Makhmudov, F., & Cho, Y. I. (2021). LDA-based topic modeling sentiment analysis using topic/document/sentence (TDS) model. Applied Sciences, 11(23), 11091.
- García-Grimau, E., De la Vega, R., De Arce, R., & Casado, A. (2021). Attitudes toward and susceptibility to doping in Spanish elite and national-standard track and field athletes: An examination of the Sport Drug Control Model. Frontiers in Psychology, 12, 679001.
- Heuberger, J. A., Henning, A., Cohen, A. F., & Kayser, B. (2022). Dealing with doping. A plea for better science, governance and education. British Journal of Clinical Pharmacology, 88(2), 566–578.
- Nanda, G., Jaiswal, A., Castellanos, H., Zhou, Y., Choi, A., & Magana, A. J. (2023). Evaluating the Coverage and Depth of Latent Dirichlet Allocation Topic Model in Comparison with Human Coding of Qualitative Data: The Case of Education Research. Machine Learning and Knowledge Extraction, 5(2), 473–490.
- Olympian Sharron Davies reveals death threats during trans debate. (2022). https://nypost.com/2022/06/21/olympian-sharron-davies-reveals-death-threats-during-trans-debate/
- Panja, T. (2019). Russia banned from Olympics and global sports for 4 years over doping. The New York Times, 9.
- Perishable. (2019, May 1). Effects of Performance-Enhancing Drugs | USADA. https://www.usada.org/athletes/substances/effects-of-performance-enhancing-drugs/
- Pöppel, K. (2021). Efficient ways to combat doping in a sports education context!? A systematic review on doping prevention measures focusing on young age groups. Frontiers in Sports and Active Living, 3, 673452.
- Praveen, S., Ittamalla, R., & Deepak, G. (2021). Analyzing the attitude of Indian citizens towards COVID-19 vaccine–A text analytics study. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 15(2), 595–599.
- Reardon, C. L., & Creado, S. (2014). Drug abuse in athletes. Substance Abuse and Rehabilitation, 5, 95–105. https://doi.org/10.2147/SAR.S53784
- Sefiha, O., & Reichman, N. (2017). Social media and the doping of sport surveillance. Sociology Compass, 11(10), e12509.
- Singh, M., Jakhar, A. K., & Pandey, S. (2021). Sentiment analysis on the impact of coronavirus in social life using the BERT model. Social Network Analysis and Mining, 11(1), 33.
- Subramanian, D. (2021, August 10). Scrape Tweets using snscrape and Build a Sentiment Classifier. Medium. https://pub.towardsai.net/scraping-tweets-using-snscrape-and-building-sentiment-classifier-13811dadd11d
- SV, P., & Ittamalla, R. (2022). General public’s attitude toward governments implementing digital contact tracing to curb COVID-19–a study based on natural language processing. International Journal of Pervasive Computing and Communications, 18(5), 485–490.
- Uyar, Y., Gentile, A., Uyar, H., Erdeveciler, Ö., Sunay, H., Mîndrescu, V., Mujkic, D., & Bianco, A. (2022). Competition, gender equality, and doping in sports in the red queen effect perspective. Sustainability, 14(5), 2490.
- Vernikou, S., Lyras, A., & Kanavos, A. (2022). Multiclass sentiment analysis on COVID-19-related tweets using deep learning models. Neural Computing and Applications, 34(22), 19615–19627.
- Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731–5780.
- Xu, Q. (2023). Competing as the First Out Transgender Female Olympian: A Twitter Network Analysis of Laurel Hubbard During the 2020 Tokyo Games. Communication & Sport, 11(5), 854–878. https://doi.org/10.1177/21674795221090422
Details
| Primary Language | English |
|---|---|
| Subjects | Nutrition and Dietetics (Other), Exercise Physiology |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | March 29, 2024 |
| Publication Date | |
| Submission Date | February 7, 2024 |
| Acceptance Date | March 17, 2024 |
| Published in Issue | Year 2024 Volume: 2 Issue: 1 |
Burdur Mehmet Akif Ersoy University Journal of Sports Sciences is a scientific and refereed journal published twice a year in June and December.
Burdur Mehmet Akif Ersoy University Journal of Sport Sciences is licensed under Creative Commons Attribution-NonCommercial 4.0 International License (CC BY NC).