Sistematik Derlemeler ve Meta Analiz

Sentiment Analysis in Communication Studies: A Bibliographic Study on Web of Science Indexed Articles

Sayı: 73 23 Temmuz 2026
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Sentiment Analysis in Communication Studies: A Bibliographic Study on Web of Science Indexed Articles

Öz

This study aims to map the intellectual structure, chronological evolution, and social networks of sentiment analysis research within the field of communication. As digital text data grows, text-as-data methods have become central to communication studies; however, a comprehensive bibliometric evaluation of this domain remains limited. The research analyzes 298 articles indexed in the Web of Science (SSCI and ESCI) between 2011 and 2025 using the PRISMA protocol. A hybrid methodological approach was adopted, combining computational bibliometric analysis via the R-bibliometrix package with manual quantitative content analysis. The findings reveal that sentiment analysis related studies experienced a latency period between 2011 and 2015, followed by exponential growth, particularly after 2020. Thematically, the literature clusters around political communication, journalism, and crisis communication, with a heavy reliance on social media data. While the United States leads in total scientific production, European countries demonstrate higher centrality in international collaboration networks. A critical finding is the field’s platform dependency, as 32% of the studies exclusively rely on Twitter (X) data. The study concludes that while sentiment analysis has matured from an experimental technique to a core methodology in communication, it risks methodological stagnation due to data source limitations. Future research is suggested to focus on multimodal analysis and diverse digital platforms to overcome these constraints.

Anahtar Kelimeler

Sentiment Analysis, Communication Studies, Bibliometric Analysis, Text Mining, Web of Science.

Etik Beyan

Ethical committee approval is not required for this study, as it utilizes publicly available social media data and contains no personal or confidential information.

Kaynakça

  1. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  2. Asur, S., & Huberman, B. A. (2010). Predicting the Future with Social Media. 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 492–499. https://doi.org/10.1109/WI- IAT.2010.63
  3. Barkur, G., Vibha, & Kamath, G. B. (2020). Sentiment analysis of nationwide lockdown due to COVID 19 outbreak: Evidence from India. Asian Journal of Psychiatry, 51, 102089. https://doi.org/10.1016/j.ajp.2020.102089
  4. Benoit, K. (2020). Text as Data: An Overview. In Luigi Curini & R. Franzese (Eds.), The SAGE Handbook of Research Methods in Political Science and International Relations. SAGE Publications Ltd. https://methods.sagepub.com/book/research-methods-in-political-science-and-international-relations
  5. Bermingham, A., & Smeaton, A. (2011). On using Twitter to monitor political sentiment and predict election results. 2–10.
  6. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.
  7. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
  8. Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8. https://doi.org/10.1016/j.jocs.2010.12.007
  9. Ceron, A., Curini, L., Iacus, S. M., & Porro, G. (2014). Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society, 16(2), 340–358. https://doi.org/10.1177/1461444813480466
  10. Dahal, B., Kumar, S. A. P., & Li, Z. (2019). Topic modeling and sentiment analysis of global climate change tweets. Social Network Analysis and Mining, 9(1), 24. https://doi.org/10.1007/s13278-019-0568-8

Kaynak Göster

APA
Demirel, S. (2026). Sentiment Analysis in Communication Studies: A Bibliographic Study on Web of Science Indexed Articles. İletişim Kuram ve Araştırma Dergisi, 73, 302-330. https://doi.org/10.47998/ikad.1835598
AMA
1.Demirel S. Sentiment Analysis in Communication Studies: A Bibliographic Study on Web of Science Indexed Articles. İletişim Kuram ve Araştırma Dergisi. 2026;(73):302-330. doi:10.47998/ikad.1835598
Chicago
Demirel, Sadettin. 2026. “Sentiment Analysis in Communication Studies: A Bibliographic Study on Web of Science Indexed Articles”. İletişim Kuram ve Araştırma Dergisi, sy 73: 302-30. https://doi.org/10.47998/ikad.1835598.
EndNote
Demirel S (01 Temmuz 2026) Sentiment Analysis in Communication Studies: A Bibliographic Study on Web of Science Indexed Articles. İletişim Kuram ve Araştırma Dergisi 73 302–330.
IEEE
[1]S. Demirel, “Sentiment Analysis in Communication Studies: A Bibliographic Study on Web of Science Indexed Articles”, İletişim Kuram ve Araştırma Dergisi, sy 73, ss. 302–330, Tem. 2026, doi: 10.47998/ikad.1835598.
ISNAD
Demirel, Sadettin. “Sentiment Analysis in Communication Studies: A Bibliographic Study on Web of Science Indexed Articles”. İletişim Kuram ve Araştırma Dergisi. 73 (01 Temmuz 2026): 302-330. https://doi.org/10.47998/ikad.1835598.
JAMA
1.Demirel S. Sentiment Analysis in Communication Studies: A Bibliographic Study on Web of Science Indexed Articles. İletişim Kuram ve Araştırma Dergisi. 2026;:302–330.
MLA
Demirel, Sadettin. “Sentiment Analysis in Communication Studies: A Bibliographic Study on Web of Science Indexed Articles”. İletişim Kuram ve Araştırma Dergisi, sy 73, Temmuz 2026, ss. 302-30, doi:10.47998/ikad.1835598.
Vancouver
1.Sadettin Demirel. Sentiment Analysis in Communication Studies: A Bibliographic Study on Web of Science Indexed Articles. İletişim Kuram ve Araştırma Dergisi. 01 Temmuz 2026;(73):302-30. doi:10.47998/ikad.1835598