Deep Learning–Based Sentiment and Topic Analysis of Turkish Football Fans on X Platform

Creative Commons License

Kayakuş M., Yiğit Açıkgöz F., Erdoğan D.

CONCURRENCY COMPUTATION PRACTICE AND EXPERIENCE, cilt.37, sa.23-24, ss.1-20, 2025 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37 Sayı: 23-24
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1002/cpe.70275
  • Dergi Adı: CONCURRENCY COMPUTATION PRACTICE AND EXPERIENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1-20
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Akdeniz Üniversitesi Adresli: Evet

Özet

This study examines social media fan comments using big data analytics to extract meaningful insights for football clubs. Comments from Turkey's top football clubs—Beşiktaş, Fenerbahçe, Galatasaray, and Trabzonspor—posted on the X platform between May 26, 2024, and November 11, 2024, were analyzed with sentiment analysis and deep learning techniques. The dataset of 20,000 comments was preprocessed and classified based on sentiment using a deep learning model implemented in Python via the X API. Additionally, text mining and topic modeling techniques identified frequently used words and key themes in the comments. The model's performance was evaluated using sensitivity, specificity, accuracy, and F1 score metrics. Sentiment analysis results demonstrated high performance, with Beşiktaş achieving precision, recall, and F1 scores of 0.957, 0.941, and 0.949, respectively, while Fenerbahçe scored 0.968, 0.941, and 0.954. Trabzonspor had the highest proportion of negative sentiment at 25.6%, reflecting the most critical fanbase. These findings highlight the effectiveness of deep learning and sentiment analysis in assessing fan engagement on social media, offering valuable insights for football clubs to better understand supporter sentiment and refine their strategies.