SavedAdd your journal

Journals / Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi / 2026 / Cilt: 14 Sayı: 2

Digital Voter Behavior: A Text Mining Approach to Analyzing Political Party Perceptions on X

Dijital Seçmen Davranışı: X Üzerinden Siyasal Parti Algılarının Metin Madenciliği Yaklaşımıyla İncelenmesi

Ahmet Doğan(OSMANİYE KORKUT ATA ÜNİVERSİTESİ)0000-0002-7116-3558
Ahmet Denizhan(OSMANİYE KORKUT ATA ÜNİVERSİTESİ)0000-0002-8655-9395
Ahmet Yurtsal(Hatay Mustafa Kemal University)0000-0003-0523-3519
Pages
1264–194
Publication Date
2026-08-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results indicate similar classification performance between the two algorithms, while sentiment distributions varied across political parties and sentiment categories.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Social media provides an important source for analyzing public opinion and societal trends.
📊
Key Findings & Data:More than 60,000 tweets collected via the X API on May 11, 12, and 14, 2023, were used to select a balanced dataset of 1,250 tweets, with 250 tweets per party.
💡
Conclusion & Impact:The results indicate similar classification performance between the two algorithms, while sentiment distributions varied across political parties and sentiment categories.
🔬Design:Araştırma Makalesi
📍Scope:2023 Verileri
⚙️Variables:Duygu Analizi · Metin Madenciliği · Veri Madenciliği

Abstract

Social media provides an important source for analyzing public opinion and societal trends. This study examines sentiment toward five political parties in Türkiye using sentiment analysis of Turkish tweets. More than 60,000 tweets collected via the X API on May 11, 12, and 14, 2023, were used to select a balanced dataset of 1,250 tweets, with 250 tweets per party. Tweets were classified as positive, neutral, or negative and analyzed using Naive Bayes and Decision Tree algorithms. The results indicate similar classification performance between the two algorithms, while sentiment distributions varied across political parties and sentiment categories. The study contributes to research on the relationship between social media and politics by demonstrating the potential of sentiment analysis for evaluating political discourse.

Özet

Sosyal medya, kamuoyu ve toplumsal eğilimlerin analizinde önemli bir veri kaynağıdır. Bu çalışmada, Türkiye’deki beş siyasi partiye yönelik duygu eğilimleri, Türkçe tweetler üzerinden duygu analizi yöntemiyle incelenmiştir. X API aracılığıyla 11, 12 ve 14 Mayıs 2023 tarihlerinde toplanan 60.000’den fazla tweet içerisinden her parti için 250 olmak üzere toplam 1.250 tweet seçilmiştir. Tweetler pozitif, nötr ve negatif olarak sınıflandırılmış; Naive Bayes ve Karar Ağacı algoritmalarıyla analiz edilmiştir. Bulgular, algoritmaların benzer sınıflandırma performansı gösterdiğini ve duygu dağılımlarının parti ve duygu kategorisine göre değiştiğini ortaya koymaktadır. Çalışma, sosyal medya ve siyaset ilişkisini inceleyen araştırmalara katkı sağlamaktadır.

🔖

Cite This Article

Doğan, A., Denizhan, A., Yurtsal, A. (2026). Digital Voter Behavior: A Text Mining Approach to Analyzing Political Party Perceptions on X. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, Cilt: 14 Sayı: 2, 1264–194. https://doi.org/10.18506/anemon.1776228

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (44)

  1. Afuan, L., Khanza, M., & Hasyati, A. Z. (2025). Enhancing sentiment analysis of the 2024 Indonesian presidential inauguration on X using SMOTE-optimized Naive Bayes classifier. Jurnal Teknik Informatika (JUTIF), 6(1), 325–333. https://doi.org/10.52436/1.jutif.2025.6.1.4290
  2. Akın, B., & Şimşek, U. T. G. (2018). Creating value through social media analytics: Future orientation through sentiment analysis. Mehmet Akif Ersoy University Journal of Economics and Administrative Sciences Faculty, 5(3), 797–811.
  3. Akpınar, H. (2000). Knowledge discovery and data mining in databases. Istanbul University Journal of the Faculty of Business Administration, 29(1), 1–22.
  4. Asokere, M., Wusu, A., & Olabanjo, O. (2025). Twitter (X) as an electoral barometer: Systematic evidence from sentiment analysis of Twitter data. International Journal of Information Technology, 1–24. https://doi.org/10.1007/s41870-025-03039-1
  5. Aygün, F. (2024). Sentiment analysis from speech signals using machine learning methods (Master’s thesis). Yıldız Technical University, Graduate School of Natural and Applied Sciences.
  6. Banea, C., Mihalcea, R., Wiebe, J., & Hassan, S. (2008). Multilingual subjectivity analysis using machine translation. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (pp. 127–135). https://doi.org/10.3115/1613715.1613734
  7. Bermingham, A., & Smeaton, A. F. (2010). Classifying sentiment in microblogs: Is brevity an advantage? In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (pp. 1833–1836). https://doi.org/10.1145/1871437.1871741
  8. Bırtıl, F. S. (2011). Analysis of the reasons for academic failure of vocational high school female students using data mining techniques (Master’s thesis). Afyon Kocatepe University, Graduate School of Natural and Applied Sciences.
  9. Ceng, E. (2018). A political analysis of Twitter usage as a perception management tool. Erciyes Communication Journal, 5(4), 663–689. https://doi.org/10.17680/erciyesiletisim.419956
  10. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104
  11. Daşgın, R. (2023). Sentiment analysis of course reviews in massive open online course platforms using text mining (Master’s thesis). Aksaray University, Graduate School of Social Sciences.
  12. Djema, G. G. F., & Suria, O. (2025). Public sentiment analysis of Danantara policy through social media X using SVM and Random Forest. INOVTEK Polbeng – Seri Informatika, 10(2), 1207–1217. https://doi.org/10.35314/rcr21h75
  13. Doğan, A., & Kara, N. (2025). Lexicon-based sentiment analysis: Comparison of satisfaction levels of e-commerce sites based on social media posts. Osmaniye Korkut Ata University Journal of the Institute of Science and Technology, 8(4), 1624–1643. https://doi.org/10.47495/okufbed.1604591
  14. Elmas, B. M. (2024). Sentiment analysis of online reviews using deep learning methods (Master’s thesis). Erciyes University, Graduate School of Social Sciences.
  15. Ercil Çağıltay, N., & Tokdemir, G. (2010). Database systems: From theory to practice. Ada Publishing. Güçdemir, Y., Mengü, S. Ç., & Günay, K. (2020). An examination of candidate leaders’ tweet usage during the Istanbul Metropolitan Municipality elections in the context of big data text mining. Connectist: Istanbul University Journal of Communication Sciences, (59), 161–191.
  16. Güneş, Y., & Arıkan, M. (2025). X (Twitter) sentiment analysis based on a hybrid approach: An application for online food ordering. Journal of Information Technologies, 18(2), 143–167. https://doi.org/10.17671/gazibtd.1616709
  17. Gürbüz, G., & Sayımer, İ. (2024). An interaction study on the effects of social media on voters’ political decisions: Twitter content analysis of the May 14, 2023 presidential elections. Electronic Cumhuriyet Communication Journal, 6(1), 38–52. https://doi.org/10.54089/ecider.1488943
  18. Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann. Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University – Engineering Sciences, 30, 330–338.
  19. Kalikov, A. (2006). Data mining and an e-commerce application (Master’s thesis). Gazi University, Graduate School of Natural and Applied Sciences.
  20. Kansal, R., & Diwaker, C. (2025). Efficiency determination of various machine learning techniques for sentiment analysis on social media platforms. Engineering, Technology & Applied Science Research, 15(4), 5584–5589. https://doi.org/10.48084/etasr.11158
  21. Kara, N. (2023). Determining satisfaction levels toward e-commerce sites in Turkey using sentiment analysis based on social media posts (Master’s thesis). Osmaniye Korkut Ata University, Graduate School of Social Sciences.
  22. Kaynar, O., Görmez, Y., Yıldız, M., & Albayrak, A. (2016). Sentiment analysis using machine learning methods. In International Artificial Intelligence and Data Processing Symposium (pp. 234–241).
  23. Khalid, N. S. K. (2024). Sentiment analysis of store reviews using deep learning: The case of Amazon (Master’s thesis). Çankırı Karatekin University, Graduate School of Natural and Applied Sciences.
  24. Khatua, A., Khatua, A., & Cambria, E. (2020). Predicting political sentiments of voters from Twitter in multi-party contexts. Applied Soft Computing, 97, 106743. https://doi.org/10.1016/j.asoc.2020.106743
  25. Kına, E. (2022). Detection of the trends of football fans in Turkey by machine learning algorithms using Twitter data. International Journal of Scientific and Engineering Research, 13(4).
  26. Kuyucu, M. (2013). Social media and social networks as new communication tools: An update. In New Communication Technologies and New Media (pp. 114–151).
  27. Liscano, Y., Anillo Arrieta, L. A., Montenegro, J. F., Prieto-Alvarado, D., & Ordoñez, J. (2025). Early warning of infectious disease outbreaks using social media and digital data: A scoping review. International Journal of Environmental Research and Public Health, 22(7), 1104. https://doi.org/10.3390/ijerph22071104
  28. Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press.
  29. McCallum, A., & Nigam, K. (1998). A comparison of event models for Naive Bayes text classification. In AAAI-98 Workshop on Learning for Text Categorization (pp. 41–48).
  30. Oğlakcı, B., & Uzun, A. (2024). Social media data mining as a new approach to electoral geography. Journal of Geographical Sciences, 22(1), 137–156.
  31. Onan, A. (2017). Sentiment analysis based on machine learning methods on Twitter messages. Journal of Management Information Systems, 3(2), 1–14.
  32. Onyenwe, I., Nwagbo, S., Mbeledogu, N., & Onyedinma, E. (2020). The impact of political party/candidate on election results from a sentiment analysis perspective using #AnambraDecides2017 tweets. Social Network Analysis and Mining, 10(1), 55.
  33. O’Reilly, T., & Milstein, S. (2011). The Twitter book. O’Reilly Media. Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (pp. 1320–1326).
  34. Pek, M. (2019). Sentiment analysis from social media data using machine learning methods (Master’s thesis). Istanbul Commerce University, Graduate School of Natural and Applied Sciences.
  35. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/BF00116251
  36. Rita, P., António, N., & Afonso, A. P. (2023). Social media discourse and voting decisions influence: Sentiment analysis in tweets during an electoral period. Social Network Analysis and Mining, 13(1), 46.
  37. Savaş, S. (2022). Digital participation analysis using text mining: The 2017 Turkish social media referendum. Acta Infologica, 6(1), 65–81.
  38. Smith-Mutegi, D., Mamo, Y., Kim, J., Crompton, H., & McConnell, M. (2025). Perceptions of STEM education and artificial intelligence: A Twitter (X) sentiment analysis. International Journal of STEM Education, 12(1), 9.
  39. Song, C., Chen, S., Cai, X., & Chen, H. (2024). Sentiment analysis of Spanish political party tweets using pre-trained language models. arXiv Preprint. https://arxiv.org/abs/2411.04862
  40. Tokcaer, S. (2021). Sentiment analysis in Turkish texts. Yaşar University E-Journal, 16(63), 1514–1534. Townsend, L., & Wallace, C. (2016). Social media research: A guide to ethics. University of Aberdeen.
  41. Ul Zahra, A., & Sadiq, A. H. B. (2025). Reimagining gender narratives: A sentiment analysis of evolving representation of female celebrities through X posts. Contemporary Journal of Social Science Review, 3(1), 93–121.
  42. Wang, J., Gong, H., & Guo, X. (2025). Multi-feature fusion graph attention network for aspect-based sentiment analysis. Knowledge-Based Systems, 114084. https://doi.org/10.1016/j.knosys.2025.114084
  43. Zhang, L., Hall, M., & Bastola, D. (2018). Utilizing Twitter data for analysis of chemotherapy. International Journal of Medical Informatics, 120, 92–100.
  44. Zhao, X., Li, X., Jiang, R., & Tang, B. (2025). Decoupled cross-attribute correlation network for multimodal sentiment analysis. Information Fusion, 117, 102897.