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An Analysis of Graduate Theses on Sentiment Analysis Published in the YÖK Thesis Database in Turkey between 2011 and 2024

Yıl 2025, Cilt: 7 Sayı: 2, 112 - 129, 31.12.2025
https://doi.org/10.53694/bited.1713137

Öz

Sentiment analysis, which aims to identify positive, negative, or neutral sentiments in texts, is one of the data-driven methods that has found application in many disciplines today. The aim of this study is to reveal academic trends in Turkey by examining postgraduate theses containing the term “sentiment analysis” in their titles, available at the YÖK National Thesis Center, based on different variables. A total of 249 theses were evaluated based on criteria such as publication year, level, language of writing, university, institute, and department. The findings show that the number of such studies has increased over time and is particularly concentrated at the master's level. While 2024 stands out as the year with the highest number of master's theses, 2019 and 2024 are notable for doctoral theses. Turkish is more commonly used in master's theses, while English is more prevalent at the doctoral level. Among universities, Bahçeşehir University ranks first in master's theses, while Hacettepe University ranks first in doctoral theses. Analyses using text mining techniques such as n-gram and word cloud reveal that most studies are based on social media data. The findings obtained are indicative of future research in the field.

Kaynakça

  • Agarwal, S. (2022). Deep learning-based sentiment analysis: Establishing customer dimension as the lifeblood of business management. Global Business Review, 23(1), 119-136.
  • Amado, A., Cortez, P., Rita, P., & Moro, S. (2018). Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics, 24(1), 1-7.
  • Alghazzawi, D. M., Alquraishee, A. G. A., Badri, S. K., & Hasan, S. H. (2023). ERF-XGB: Ensemble random forest-based XG boost for accurate prediction and classification of e-commerce product review. Sustainability, 15(9), 7076.
  • Alzamil, Z., Appelbaum, D., & Nehmer, R. (2020). An ontological artifact for classifying social media: Text mining analysis for financial data. International Journal of Accounting Information Systems, 38, 100469. Arnold, S. (2011). From sentiment to applications. KM World, 20(7), 1-20.
  • Baqach, A., & Battou, A. (2023). CLAS: A new deep learning approach for sentiment analysis from Twitter data. Multimedia Tools and Applications, 82(30), 47457-47475.
  • Chen, X., & Xie, H. (2020). A structural topic modeling-based bibliometric study of sentiment analysis literature. Cognitive Computation, 12, 1097-1129.
  • Deshpande, M., & Sarkar, A. (2010). BI and sentiment analysis. Business Intelligence Journal, 15(2), 41-49. Devika, M. D., Sunitha, C., & Ganesh, A. (2016). Sentiment analysis: A comparative study on different approaches. Procedia Computer Science, 87, 44-49.
  • Elangovan, D., & Subedha, V. (2023). Adaptive Particle Grey Wolf optimizer with deep learning-based sentiment analysis on online product reviews. Engineering, Technology & Applied Science Research, 13(3), 10989-10993.
  • Fan, Y., Teo, H. P., & Wan, W. X. (2021). Public transport, noise complaints, and housing: Evidence from sentiment analysis in Singapore. Journal of Regional Science, 61(3), 570-596.
  • Ghag, K. V., & Shah, K. (2015). Comparative analysis of effect of stopwords removal on sentiment classification. In 2015 International Conference on Computer, Communication and Control (IC4) (pp. 1-6). IEEE.
  • Gohil, S., Vuik, S., & Darzi, A. (2018). Sentiment analysis of health care tweets: review of the methods used. JMIR Public Health And Surveillance, 4(2), e5789.
  • Hashimi, H., Hafez, A., & Mathkour, H. (2015). Selection criteria for text mining approaches. Computers in Human Behavior, 51, 729-733.
  • Hemalatha, I., Varma, G. S., & Govardhan, A. (2012). Preprocessing the informal text for efficient sentiment analysis. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 1(2), 58-61.
  • Hidayati, N. N. (2023). Improving aspect-based sentiment analysis for hotel reviews with Latent Dirichlet Allocation and machine learning algorithms. Register, 9(2), 144-159.
  • Jung, H., & Lee, B. G. (2020). Research trends in text mining: Semantic network and main path analysis of selected journals. Expert Systems with Applications, 162, 113851.
  • Kar, A. K., & Dwivedi, Y. K. (2020). Theory building with big data-driven research–Moving away from the “What” towards the “Why”. International Journal of Information Management, 54, 102205.
  • Koppel, M., & Schler, J. (2006). The importance of neutral examples for learning sentiment. Computational Intelligence, 22(2), 100–109.
  • Krosuri, L. R., & Aravapalli, R. S. (2023). Feature level fine grained sentiment analysis using boosted long short-term memory with improvised local search whale optimization. PeerJ Computer Science, 9, e1336.
  • Lau, K. N., Lee, K. H., & Ho, Y. (2005). Text mining for the hotel industry. Cornell Hotel and Restaurant Administration Quarterly, 46(3), 344-362.
  • Li, D., Zhang, Y., & Li, C. (2019). Mining public opinion on transportation systems based on social media data. Sustainability, 11(15), 4016.
  • Li, L., Mao, Y., Wang, Y., & Ma, Z. (2022). How has airport service quality changed in the context of COVID-19: A data-driven crowdsourcing approach based on sentiment analysis. Journal of Air Transport Management, 105, 102298.
  • Loukili, M., Messaoudi, F., & El Ghazi, M. (2023). Sentiment Analysis of product reviews for E-commerce recommendation based on Machine Learning. International Journal of Advances in Soft Computing & Its Applications, 15(1).
  • Lucini, F. R., Fogliatto, F. S., da Silveira, G. J., Neyeloff, J. L., Anzanello, M. J., Kuchenbecker, R. S., & Schaan, B. D. (2017). Text mining approach to predict hospital admissions using early medical records from the emergency department. International Journal of Medical Informatics, 100, 1-8.
  • Marangoz, M., Yeşildağ, B., & Saltık, I. A. (2012). E-ticaret işletmelerinin web ve sosyal ağ sitelerinin içerik analizi yöntemiyle incelenmesi. İnternet Uygulamaları ve Yönetimi Dergisi, 3(2), 53-78.
  • Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113.
  • Miley, F., & Read, A. (2011). Using word clouds to develop proactive learners. Journal of the Scholarship of Teaching and Learning, 91-110.
  • Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2021). Deep learning--based text classification: a comprehensive review. ACM Computing Surveys (CSUR), 54(3), 1-40.
  • Mishra, D. N., & Panda, R. K. (2022). Decoding customer experiences in rail transport service: application of hybrid sentiment analysis. Public Transport, 1-30.
  • Mittal, D., & Agrawal, S. R. (2022). Determining banking service attributes from online reviews: text mining and sentiment analysis. International Journal of Bank Marketing, 40(3), 558-577.
  • Mujahid, M., Lee, E., Rustam, F., Washington, P. B., Ullah, S., Reshi, A. A., & Ashraf, I. (2021). Sentiment analysis and topic modeling on tweets about online education during COVID-19. Applied Sciences, 11(18), 8438.
  • Omran, T. M., Sharef, B. T., Grosan, C., & Li, Y. (2023). Transfer learning and sentiment analysis of Bahraini dialects sequential text data using multilingual deep learning approach. Data & Knowledge Engineering, 143, 102106.
  • Onan, A. (2017). Twitter mesajlari üzerinde makine öğrenmesi yöntemlerine dayali duygu analizi. Yönetim Bilişim Sistemleri Dergisi, 3(2), 1-14.
  • Prottasha, N. J., Sami, A. A., Kowsher, M., Murad, S. A., Bairagi, A. K., Masud, M., & Baz, M. (2022). Transfer learning for sentiment analysis using BERT based supervised fine-tuning. Sensors, 22(11), 4157.
  • Sailunaz, K., & Alhajj, R. (2019). Emotion and sentiment analysis from Twitter text. Journal of Computational Science, 36, 101003.
  • Solairaj, A., Sugitha, G., & Kavitha, G. (2023). Enhanced Elman spike neural network based sentiment analysis of online product recommendation. Applied Soft Computing, 132, 109789.
  • Susanti, A. R., Djatna, T., & Kusuma, W. A. (2017). Twitter’s sentiment analysis on GSM services using Multinomial Naïve Bayes. Telkomnika (Telecommunication Computing Electronics and Control), 15(3), 1354-1361.
  • Taboada, M. (2016). Sentiment analysis: An overview from linguistics. Annual Review of Linguistics, 2(1), 325-347.
  • Takcı, H., & Baktır, N. (2018). Büyük veri yaklaşımıyla birden çok bilgi erişim merkezinin kolektif kullanımı. Bilişim Teknolojileri Dergisi, 11(2), 123-129.
  • Varol, M. Ç., & Varol, E. (2021). Yeni medyada duygu analizi üzerine bir değerlendirme: Bilgisayar mühendisliği bilimleri doktora tezleri incelemesi. N. Pembecioğlu, N. Sezer, U. Gündüz & N. Akgün-Çomak. İletişim araştırmaları ve film çözümlemeleri II: Dijital çağda medya, 79-97.
  • Vijayarani, S., Ilamathi, M. J., & Nithya, M. (2015). Preprocessing techniques for text mining-an overview. International Journal of Computer Science & Communication Networks, 5(1), 7-16.
  • 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.
  • Zuo, W., Bai, W., Zhu, W., He, X., & Qiu, X. (2022). Changes in service quality of sharing accommodation: Evidence from Airbnb. Technology in Society, 71, 102092.

Türkiye’de 2011-2024 Yılları Arasında Yök Tez Veri Tabanında Yayımlanan Duygu Analizi ile İlgili Lisansüstü Tezlerin İncelenmesi

Yıl 2025, Cilt: 7 Sayı: 2, 112 - 129, 31.12.2025
https://doi.org/10.53694/bited.1713137

Öz

Metinlerdeki olumlu, olumsuz ya da nötr duyguları tespit etmeye yönelik duygu analizi, günümüzde birçok disiplinde uygulama alanı bulan veri odaklı yöntemlerden biridir. Çalışmanın amacı, YÖK Ulusal Tez Merkezi’nde yer alan ve başlığında “duygu analizi” geçen lisansüstü tezleri farklı değişkenler üzerinden inceleyerek Türkiye’deki akademik eğilimleri ortaya koymaktır. Toplam 249 tez; yayın yılı, düzey, yazım dili, üniversite, enstitü ve anabilim dalı gibi ölçütlerle değerlendirilmiştir. Elde edilen bulgular, söz konusu çalışmaların zamanla arttığını ve özellikle yüksek lisans düzeyinde yoğunlaştığını göstermektedir. 2024, en fazla yüksek lisans tezinin yazıldığı yıl olarak öne çıkarken, doktora düzeyinde 2019 ve 2024 yılları dikkat çekmektedir. Yüksek lisans tezlerinde Türkçe, doktora düzeyinde ise İngilizce kullanımının daha yaygın olduğu görülmüştür. Üniversiteler arasında Bahçeşehir Üniversitesi yüksek lisans; Hacettepe Üniversitesi doktora tezlerinde ilk sırada yer almaktadır. N-gram ve kelime bulutu gibi metin madenciliği teknikleriyle yapılan analizlerde, çalışmaların çoğunlukla sosyal medya verilerini temel aldığı görülmektedir. Elde edilen bulgular, alandaki gelecek araştırmalara yön gösterici niteliktedir.

Etik Beyan

Bulunmamaktadır.

Destekleyen Kurum

Bulunmamaktadır.

Teşekkür

Bulunmamaktadır.

Kaynakça

  • Agarwal, S. (2022). Deep learning-based sentiment analysis: Establishing customer dimension as the lifeblood of business management. Global Business Review, 23(1), 119-136.
  • Amado, A., Cortez, P., Rita, P., & Moro, S. (2018). Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics, 24(1), 1-7.
  • Alghazzawi, D. M., Alquraishee, A. G. A., Badri, S. K., & Hasan, S. H. (2023). ERF-XGB: Ensemble random forest-based XG boost for accurate prediction and classification of e-commerce product review. Sustainability, 15(9), 7076.
  • Alzamil, Z., Appelbaum, D., & Nehmer, R. (2020). An ontological artifact for classifying social media: Text mining analysis for financial data. International Journal of Accounting Information Systems, 38, 100469. Arnold, S. (2011). From sentiment to applications. KM World, 20(7), 1-20.
  • Baqach, A., & Battou, A. (2023). CLAS: A new deep learning approach for sentiment analysis from Twitter data. Multimedia Tools and Applications, 82(30), 47457-47475.
  • Chen, X., & Xie, H. (2020). A structural topic modeling-based bibliometric study of sentiment analysis literature. Cognitive Computation, 12, 1097-1129.
  • Deshpande, M., & Sarkar, A. (2010). BI and sentiment analysis. Business Intelligence Journal, 15(2), 41-49. Devika, M. D., Sunitha, C., & Ganesh, A. (2016). Sentiment analysis: A comparative study on different approaches. Procedia Computer Science, 87, 44-49.
  • Elangovan, D., & Subedha, V. (2023). Adaptive Particle Grey Wolf optimizer with deep learning-based sentiment analysis on online product reviews. Engineering, Technology & Applied Science Research, 13(3), 10989-10993.
  • Fan, Y., Teo, H. P., & Wan, W. X. (2021). Public transport, noise complaints, and housing: Evidence from sentiment analysis in Singapore. Journal of Regional Science, 61(3), 570-596.
  • Ghag, K. V., & Shah, K. (2015). Comparative analysis of effect of stopwords removal on sentiment classification. In 2015 International Conference on Computer, Communication and Control (IC4) (pp. 1-6). IEEE.
  • Gohil, S., Vuik, S., & Darzi, A. (2018). Sentiment analysis of health care tweets: review of the methods used. JMIR Public Health And Surveillance, 4(2), e5789.
  • Hashimi, H., Hafez, A., & Mathkour, H. (2015). Selection criteria for text mining approaches. Computers in Human Behavior, 51, 729-733.
  • Hemalatha, I., Varma, G. S., & Govardhan, A. (2012). Preprocessing the informal text for efficient sentiment analysis. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 1(2), 58-61.
  • Hidayati, N. N. (2023). Improving aspect-based sentiment analysis for hotel reviews with Latent Dirichlet Allocation and machine learning algorithms. Register, 9(2), 144-159.
  • Jung, H., & Lee, B. G. (2020). Research trends in text mining: Semantic network and main path analysis of selected journals. Expert Systems with Applications, 162, 113851.
  • Kar, A. K., & Dwivedi, Y. K. (2020). Theory building with big data-driven research–Moving away from the “What” towards the “Why”. International Journal of Information Management, 54, 102205.
  • Koppel, M., & Schler, J. (2006). The importance of neutral examples for learning sentiment. Computational Intelligence, 22(2), 100–109.
  • Krosuri, L. R., & Aravapalli, R. S. (2023). Feature level fine grained sentiment analysis using boosted long short-term memory with improvised local search whale optimization. PeerJ Computer Science, 9, e1336.
  • Lau, K. N., Lee, K. H., & Ho, Y. (2005). Text mining for the hotel industry. Cornell Hotel and Restaurant Administration Quarterly, 46(3), 344-362.
  • Li, D., Zhang, Y., & Li, C. (2019). Mining public opinion on transportation systems based on social media data. Sustainability, 11(15), 4016.
  • Li, L., Mao, Y., Wang, Y., & Ma, Z. (2022). How has airport service quality changed in the context of COVID-19: A data-driven crowdsourcing approach based on sentiment analysis. Journal of Air Transport Management, 105, 102298.
  • Loukili, M., Messaoudi, F., & El Ghazi, M. (2023). Sentiment Analysis of product reviews for E-commerce recommendation based on Machine Learning. International Journal of Advances in Soft Computing & Its Applications, 15(1).
  • Lucini, F. R., Fogliatto, F. S., da Silveira, G. J., Neyeloff, J. L., Anzanello, M. J., Kuchenbecker, R. S., & Schaan, B. D. (2017). Text mining approach to predict hospital admissions using early medical records from the emergency department. International Journal of Medical Informatics, 100, 1-8.
  • Marangoz, M., Yeşildağ, B., & Saltık, I. A. (2012). E-ticaret işletmelerinin web ve sosyal ağ sitelerinin içerik analizi yöntemiyle incelenmesi. İnternet Uygulamaları ve Yönetimi Dergisi, 3(2), 53-78.
  • Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113.
  • Miley, F., & Read, A. (2011). Using word clouds to develop proactive learners. Journal of the Scholarship of Teaching and Learning, 91-110.
  • Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2021). Deep learning--based text classification: a comprehensive review. ACM Computing Surveys (CSUR), 54(3), 1-40.
  • Mishra, D. N., & Panda, R. K. (2022). Decoding customer experiences in rail transport service: application of hybrid sentiment analysis. Public Transport, 1-30.
  • Mittal, D., & Agrawal, S. R. (2022). Determining banking service attributes from online reviews: text mining and sentiment analysis. International Journal of Bank Marketing, 40(3), 558-577.
  • Mujahid, M., Lee, E., Rustam, F., Washington, P. B., Ullah, S., Reshi, A. A., & Ashraf, I. (2021). Sentiment analysis and topic modeling on tweets about online education during COVID-19. Applied Sciences, 11(18), 8438.
  • Omran, T. M., Sharef, B. T., Grosan, C., & Li, Y. (2023). Transfer learning and sentiment analysis of Bahraini dialects sequential text data using multilingual deep learning approach. Data & Knowledge Engineering, 143, 102106.
  • Onan, A. (2017). Twitter mesajlari üzerinde makine öğrenmesi yöntemlerine dayali duygu analizi. Yönetim Bilişim Sistemleri Dergisi, 3(2), 1-14.
  • Prottasha, N. J., Sami, A. A., Kowsher, M., Murad, S. A., Bairagi, A. K., Masud, M., & Baz, M. (2022). Transfer learning for sentiment analysis using BERT based supervised fine-tuning. Sensors, 22(11), 4157.
  • Sailunaz, K., & Alhajj, R. (2019). Emotion and sentiment analysis from Twitter text. Journal of Computational Science, 36, 101003.
  • Solairaj, A., Sugitha, G., & Kavitha, G. (2023). Enhanced Elman spike neural network based sentiment analysis of online product recommendation. Applied Soft Computing, 132, 109789.
  • Susanti, A. R., Djatna, T., & Kusuma, W. A. (2017). Twitter’s sentiment analysis on GSM services using Multinomial Naïve Bayes. Telkomnika (Telecommunication Computing Electronics and Control), 15(3), 1354-1361.
  • Taboada, M. (2016). Sentiment analysis: An overview from linguistics. Annual Review of Linguistics, 2(1), 325-347.
  • Takcı, H., & Baktır, N. (2018). Büyük veri yaklaşımıyla birden çok bilgi erişim merkezinin kolektif kullanımı. Bilişim Teknolojileri Dergisi, 11(2), 123-129.
  • Varol, M. Ç., & Varol, E. (2021). Yeni medyada duygu analizi üzerine bir değerlendirme: Bilgisayar mühendisliği bilimleri doktora tezleri incelemesi. N. Pembecioğlu, N. Sezer, U. Gündüz & N. Akgün-Çomak. İletişim araştırmaları ve film çözümlemeleri II: Dijital çağda medya, 79-97.
  • Vijayarani, S., Ilamathi, M. J., & Nithya, M. (2015). Preprocessing techniques for text mining-an overview. International Journal of Computer Science & Communication Networks, 5(1), 7-16.
  • 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.
  • Zuo, W., Bai, W., Zhu, W., He, X., & Qiu, X. (2022). Changes in service quality of sharing accommodation: Evidence from Airbnb. Technology in Society, 71, 102092.
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Doğal Dil İşleme
Bölüm Araştırma Makalesi
Yazarlar

Sidar Ağduk 0000-0002-2927-0077

Gönderilme Tarihi 3 Haziran 2025
Kabul Tarihi 2 Ekim 2025
Yayımlanma Tarihi 31 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 7 Sayı: 2

Kaynak Göster

APA Ağduk, S. (2025). Türkiye’de 2011-2024 Yılları Arasında Yök Tez Veri Tabanında Yayımlanan Duygu Analizi ile İlgili Lisansüstü Tezlerin İncelenmesi. Bilgi ve İletişim Teknolojileri Dergisi, 7(2), 112-129. https://doi.org/10.53694/bited.1713137

      

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Bilgi ve İletişim Teknolojileri Dergisi (BİTED)

Journal of Information and Communication Technologies