KaydedilenlerDerginizi ekleyin

Dergiler / International Journal of Social Inquiry / 2026 / Cilt: 18 Sayı: 2

Netflix İçeriklerinin Duygu Analizi Yöntemi İle İncelenmesi

Analyzing Netflix Content with Sentiment Analysis Method

Adem Aksan(ULUDAĞ ÜNİVERSİTESİ, SOSYAL BİLİMLER ENSTİTÜSÜ, EKONOMETRİ (DR))0000-0003-0545-4143
Ayşe Oğuzlar(BURSA ULUDAĞ ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ, EKONOMETRİ BÖLÜMÜ)0000-0003-3228-9366
Sayfa
256–273
Yayın Tarihi
2025-08-31
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

NRC sözlük içerisinde yer alan 8 temel duygu düzeyinde analiz yapılarak bulgulara yer verilmiştir.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:İnsanların yoğun ilgisiyle birlikte digital içerik platformlarının sayısı günden güne artmaktadır.
📊
Temel Bulgular & Veri:Duygu analizi, 2000’li yılların başlangıcıyla birlikte metinlerin içerisinde yer alan duyguları ortaya çıkaran sözlük tabanlı bir tekniktir.
💡
Sonuç & Katkı:NRC sözlük içerisinde yer alan 8 temel duygu düzeyinde analiz yapılarak bulgulara yer verilmiştir.
🔬Tür:Araştırma Makalesi
📍Kapsam:2000 Verileri
⚙️Değişkenler:Duygu Sınıflandırması · Duygu Analizi · Veri Madenciliği

Özet

İnsanların yoğun ilgisiyle birlikte digital içerik platformlarının sayısı günden güne artmaktadır. Netflix’de kullanıcılar tarafından tercih edilen dijital içerik platformlarından biridir. Duygu analizi, 2000’li yılların başlangıcıyla birlikte metinlerin içerisinde yer alan duyguları ortaya çıkaran sözlük tabanlı bir tekniktir. Duygu analizi içerik geliştiricileri için de önemli bir araçtır. Bu sayede senaryo yazma, tanım metinleri oluşturma gibi konularda yazarlara katkı sağlar. Ayrıca ilgi çeken içeriklerin, metinsel açıdan analiz ederek yazarlara rehberlik eder. Bu bilgilerden yola çıkarak hazırlanan çalışmanın amacı, Netflix içeriklerine ait tanıtım metinlerini duygu analizi ile karşılaştırmaktır. Bu kapsamda, Türkiye yapımı Netflix içerikleri ile diğer ülkelere ait Netflix içeriklerini, denetimsiz öğrenme tekniği olan sözlük tabanlı duygu analizi ile karşılaştırılmıştır. NRC sözlük içerisinde yer alan 8 temel duygu düzeyinde analiz yapılarak bulgulara yer verilmiştir. Yapılan analiz sonucunda Türkiye yapımı içeriklere ait tanıtım metinlerinde korku duygusunun, diğer içeriklerde ise güven duygusunun öne çıktığı tespit edilmiştir. Türkiye yapımı içeriklerin tanıtım metinlerinin(-0,25), diğer ülkelere ait içeriklere(0,03) göre daha fazla negatif duyguya sahiptir. Ayrıca iki grup için elde edilen duygu skorları arasında %5 anlamlılık düzeyinde istatistiksel açıdan anlamlı bir farklılık olduğu tespit edilmiştir.

Abstract

The number of digital content platforms is increasing due to people's growing interest. Netflix is a popular choice among users. Since the early 2000s, sentiment analysis, a dictionary-based technique, has been used to reveal emotions in texts. It is also an important tool for content developers and contributes to writers in areas such as scriptwriting and creating descriptive texts. It also guides authors by analyzing interesting content from a textual perspective. Based on this information, this study aims to compare the promotional texts of Netflix content with sentiment analysis. In this context, Netflix content from Türkiye and Netflix content from other countries were compared using dictionary-based sentiment analysis, which is an unsupervised learning technique. The findings were analyzed using the 8 basic emotion levels in the NRC dictionary. As a result of the analysis, it was determined that the emotion of fear was prominent in the promotional texts of Türkiye-made content, while the emotion of trust was prominent in other content. Promotional texts of Turkish-made content (-0.25) have more negative emotions than content from other countries (0.03). In addition, a statistically significant difference was found between the emotion scores obtained for the two groups at a significance level of 5%.

🔖

Bu Makaleye Atıf Yap

Aksan, A., Oğuzlar, A. (2025). Netflix İçeriklerinin Duygu Analizi Yöntemi İle İncelenmesi. International Journal of Social Inquiry, Cilt: 18 Sayı: 2, 256–273. https://doi.org/10.37093/ijsi.1515251

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (67)

  1. 2024’te Dijital Platformlarda En Çok İzlenen 10 Yerli Dizi - Beyazperde.com. (n.d.). Retrieved February 2, 2025, from https://www.beyazperde.com/galerileri/diziler/galerileri-1000121575/#google_vignette
  2. Agarwal, B., & Mittal, N. (2016). Prominent Feature Extraction for Sentiment Analysis. Springer International Publishing. https://doi.org/10.1007/978-3-319-25343-5
  3. Barker, C., & Wiatrowski, M. (2017). The age of Netflix: critical essays on streaming media, digital delivery and instant access. McFarland & Company, Inc., Publishers.
  4. Bordoloi, M., & Biswas, S. K. (2023). Sentiment analysis: A survey on design framework, applications and future scopes. Artificial Intelligence Review, 56(11), 12505–12560. https://doi.org/10.1007/S10462-023-10442-2/FIGURES/3
  5. Bordwell, D., & Thompson, K. (2019). Film Art: An Introduction (12. baskı). McGraw-Hill Professional.
  6. Cambria, E., Havasi, C., & Hussain, A. (2012). SenticNet 2: A Semantic and Affective Resource for Opinion Mining and Sentiment Analysis. FLAIRS Conference.
  7. Chalaby, J. (2023). Television in the Streaming Era. In The Rise of Networks (pp. 36–55). Cambridge University Press. https://doi.org/10.1017/9781009199285.004
  8. Chintalapudi, N., Battineni, G., Canio, M. Di, Sagaro, G. G., & Amenta, F. (2021). Text mining with sentiment analysis on seafarers’ medical documents. International Journal of Information Management Data Insights, 1(1), 100005. https://doi.org/10.1016/J.JJIMEI.2020.100005
  9. Dang, Y., Zhang, Y., & Chen, H. (2010). A lexicon-enhanced method for sentiment classification: An experiment on online product reviews. IEEE Intelligent Systems, 25(4), 46–53. https://doi.org/10.1109/MIS.2009.105
  10. Demirel, S., Kahraman-Gokalp, E., & Gündüz, U. (2024). From Optimism to Concern: Unveiling Sentiments and Perceptions Surrounding ChatGPT on Twitter. International Journal of Human–Computer Interaction, 1–23. https://doi.org/10.1080/10447318.2024.2392964
  11. Fayyad Usame, P.-S. G. S. P. (1996). The KDD Process for Extracting Useful Knowledge from Volumes of Data. Communıcatıons Of The ACM, 39(11), 27–34. http://shawndra.pbworks.com/f/The KDD process for extracting useful knowledge from volumes of data.pdf
  12. Fernando Sánchez-Rada, J., Araque, O., & Iglesias, C. A. (2020). Senpy: A framework for semantic sentiment and emotion analysis services ✩. Elsevier, 190(Senpy: A framework for semantic sentiment and emotion analysis services), 105193. https://doi.org/10.1016/j.knosys
  13. Garg, P. K., Pandey, M., & Arora, M. (2019). Sentiment Analysis for Predicting the Popularity of Web Series. Communications in Computer and Information Science, 1230 CCIS, 133–140. https://doi.org/10.1007/978-981-15-5830-6_12
  14. Garg, R., Kiwelekar, A. W., Netak, L. D., & Bhate, S. S. (2021). Potential Use-Cases of Natural Language Processing for a Logistics Organization. 157–191. https://doi.org/10.1007/978-3-030-68291-0_13
  15. Gündüz, U., & Demirel, S. (2023). Metaverse-related perceptions and sentiments on Twitter: evidence from text mining and network analysis. Electronic Commerce Research, 1–31. https://doi.org/10.1007/S10660-023-09745-X/TABLES/4
  16. Gündüz, U., Demirel, S., & Tombul, I. (2024). Exploring the concept of financial domination on social media: sentiment and text analysis on Twitter. Atlantic Journal of Communication, 32(4), 602–625. https://doi.org/10.1080/15456870.2023.2178000
  17. Hanusz, Z., Tarasinska, J., & Zielinski, W. (2016). Shapiro–Wilk Test with Known Mean. REVSTAT-Statistical Journal, 14(1), 89-100–189–100. https://doi.org/10.57805/REVSTAT.V14I1.180
  18. Hernandez-Farias, I., Benedi, J. M., & Rosso, P. (2015). Applying basic features from sentiment analysis for automatic irony detection. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9117, 337–344. https://doi.org/10.1007/978-3-319-19390-8_38
  19. Hull, R. (1997). Managing Semantic Heterogeneity in Databases: A Theoretical Perspective. http://arru-db.research.bell-labs.com/user/hull/pods97-tutoridl.html. Introduction to the Syuzhet Package. (n.d.). Retrieved December 30, 2020, from https://cran.r-project.org/web/packages/syuzhet/vignettes/syuzhet-vignette.html?
  20. Jagdale, R. S., Shirsat, V. S., & Deshmukh, S. N. (2019). Sentiment analysis on product reviews using machine learning techniques. Advances in Intelligent Systems and Computing, 768, 639–647. https://doi.org/10.1007/978-981-13-0617-4_61
  21. Jang, M., Kim, D., & Baek, H. (2023). How do global audiences of TV shows take shape?: Evidence from Netflix. Applied Economics Letters, 30(3), 285–291. https://doi.org/10.1080/13504851.2021.1983916
  22. Jockers, M. M. (2020). Package “syuzhet” Type Package Title Extracts Sentiment and Sentiment-Derived Plot Arcs from Text. https://github.com/mjockers/syuzhet
  23. Kahraman, E., Demirel, S., & Gündüz, U. (2023). COVID-19 vaccines in twitter ecosystem: Analyzing perceptions and attitudes by sentiment and text analysis method. Journal of Public Health (Germany), 1–15. https://doi.org/10.1007/S10389-023-02078-X/METRICS
  24. Karami, A., Bookstaver, B., Nolan, M., & Bozorgi, P. (2021). Investigating diseases and chemicals in COVID-19 literature with text mining. International Journal of Information Management Data Insights, 1(2), 100016. https://doi.org/10.1016/J.JJIMEI.2021.100016
  25. Kumar, S., Kar, A. K., & Ilavarasan, P. V. (2021). Applications of text mining in services management: A systematic literature review. International Journal of Information Management Data Insights, 1(1), 100008. https://doi.org/10.1016/J.JJIMEI.2021.100008
  26. Li, J., & Qiu, L. (2017). A sentiment analysis method of short texts in microblog. Proceedings - 2017 IEEE International Conference on Computational Science and Engineering and IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, CSE and EUC 2017, 1, 776–779. https://doi.org/10.1109/CSE-EUC.2017.153
  27. Li, N., & Wu, D. D. (2010). Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support Systems, 48(2), 354–368. https://doi.org/10.1016/j.dss.2009.09.003
  28. Liu, B. (2012a). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–184. https://doi.org/10.2200/S00416ED1V01Y201204HLT016
  29. Liu, B. (2012b). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers.
  30. Lotz, A. D. (2022). Netflix and streaming video: the business of subscriber-funded video on demand. Polity Press.
  31. Lotz, A. D., Eklund, O., & Soroka, S. (2022). Netflix, library analysis, and globalization: rethinking mass media flows. Journal of Communication, 72(4), 511–521. https://doi.org/10.1093/JOC/JQAC020
  32. MacDowell, J. (2014). Happy Endings in Hollywood Cinema: Cliché, Convention and the Final Couple. Edinburgh University Press.
  33. MacFarland, T. W., & Yates, J. M. (2016). Mann–Whitney U Test. Introduction to Nonparametric Statistics for the Biological Sciences Using R, 103–132. https://doi.org/10.1007/978-3-319-30634-6_4
  34. Maranatha, Y. G., & Karyatun, S. (2024). Netflix Subscription Interest in Generation Z: Mobile Advertising, Service Quality, and Price Perception. 199–207. https://doi.org/10.2991/978-94-6463-394-8_20
  35. Matsumoto, D., & Hwang, H. S. (2011). Culture and Emotion. Journal of Cross-Cultural Psychology, 43(1), 91–118. https://doi.org/10.1177/0022022111420147
  36. Mesquita, B., & Walker, R. (2003). Cultural differences in emotions: a context for interpreting emotional experiences. Behaviour Research and Therapy, 41(7), 777–793. https://doi.org/10.1016/S0005-7967(02)00189-4
  37. Mohammad, S. M. (2020). Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text. Emotion Measurement, 201–237. http://arxiv.org/abs/2005.11882
  38. Mohammad, S. M., & Turney, P. D. (n.d.). Crowdsourcing a Word-Emotion Association Lexicon. http://crowdsourcing.typepad.com/cs/2006/06
  39. Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a Word-Emotion Association Lexicon. http://crowdsourcing.typepad.com/cs/2006/06
  40. Na, J. C., Khoo, C., & Wu, P. H. J. (2005). Use of negation phrases in automatic sentiment classification of product reviews. Library Collections, Acquisition and Technical Services, 29(2), 180–191. https://doi.org/10.1016/j.lcats.2005.04.007
  41. Naldi, M. (2019). A review of sentiment computation methods with R packages. https://cran.r-project.org/web/packages/syuzhet/syuz
  42. Neogi, A. S., Garg, K. A., Mishra, R. K., & Dwivedi, Y. K. (2021). Sentiment analysis and classification of Indian farmers’ protest using twitter data. International Journal of Information Management Data Insights, 1(2), 100019. https://doi.org/10.1016/J.JJIMEI.2021.100019
  43. Netflix Movies and TV Shows. (2024). https://www.kaggle.com/datasets/shivamb/netflix-shows/data
  44. Nielsen, F. Å. (2011). A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. CEUR Workshop Proceedings, 718, 93–98. http://arxiv.org/abs/1103.2903
  45. Özpay, O. (2019). Türk Korku Sinemasına Panoramik Bir Bakış ve İdeolojik İzdüşümleri. Akdeniz İletişim Dergisi, 32, 551–567.
  46. Pang, B., & Lee, L. (2004). A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. Antioch Review, 63(2), 271–278. https://doi.org/10.3115/1218955.1218990
  47. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/1500000011
  48. Peng, Y. F., & Chou, T. R. (2019). Automatic color palette design using color image and sentiment analysis. 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019, 389–392. https://doi.org/10.1109/ICCCBDA.2019.8725717
  49. R. K. Bakshi, N. Kaur, R. K. and G. K. (2016). Opinion mining and sentiment analysis. 2016 International Conference on Computing for Sustainable Global Development (INDIACom), 452–455. https://0211mxbix-y-https-ieeexplore-ieee-org.proxy.uludag.deep-knowledge.net/document/7724305
  50. Rosenbusch, H., Evans, A. M., & Zeelenberg, M. (2019). Multilevel Emotion Transfer on YouTube: Disentangling the Effects of Emotional Contagion and Homophily on Video Audiences. Social Psychological and Personality Science, 10(8), 1028–1035. https://doi.org/10.1177/1948550618820309
  51. Şaki Aydın, O. (2019). Yeni İzleme Biçimleri Ve Netflix İçerikleri: Ritzer İn Mcdonaldlaşma Tezi Ekseninde Bir Değerlendirme. Journal of International Social Research, 12(63), 1164–1172. https://doi.org/10.17719/jisr.2019.3305
  52. Sarı, Ü., & Sancaklı, P. (2020). Küreselleşmenin Dijital Platformların İçerik Tanıtımına Etkisi: Netflix Örneği. Erciyes İletişim Dergisi, 7(1), 243–260. https://doi.org/10.17680/erciyesiletisim.647463
  53. Sigismondi, P., & Ciofalo, G. (2022). Glocalization processes and new centrifugal dynamics in the international entertainment landscape: the Netflix Case in Italy. Handbook of Culture and Glocalization, 305–320. https://doi.org/10.4337/9781839109010
  54. Stanković, M. (2018). TV Series or Not? AM Časopis Za Studije Umetnosti i Medija, 17, 1–13.
  55. Tan, K. L., Lee, C. P., & Lim, K. M. (2023). A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research. In Applied Sciences (Switzerland) (Vol. 13, Issue 7). https://doi.org/10.3390/app13074550
  56. Türk Filmleri Seyirci Rekoru - İlk 100 (1989’dan günümüze) - Tüm Zamanlar - Box Office Türkiye. (n.d.). Retrieved February 2, 2025, from https://boxofficeturkiye.com/tum-zamanlar/seyirci-rekorlari/turk-filmleri
  57. Vural, A. G., Cambazoglu, B. B., Senkul, P., & Tokgoz, Z. O. (2013). A Framework for Sentiment Analysis in Turkish: Application to Polarity Detection of Movie Reviews in Turkish. In Computer and Information Sciences III (pp. 437–445). Springer London. https://doi.org/10.1007/978-1-4471-4594-3_45
  58. Wegmann, E., & Brand, M. (2020). Cognitive Correlates in Gaming Disorder and Social Networks Use Disorder: a Comparison. Current Addiction Reports, 7(3), 356–364. https://doi.org/10.1007/S40429-020-00314-Y/FIGURES/4
  59. Wiebe, J., & Riloff, E. (2005). Creating Subjective and Objective Sentence Classifiers from Unannotated Texts. In G. Alexander (Ed.), Computational Linguistics and Intelligent Text Processing (Vol. 3406, pp. 486–497). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30586-6_53
  60. Xu, Y. (2021). Re-understanding Personal and Organizational Mission with Netflix Cultural Manual. Education Reform and Development, 3(1), 34–37. https://doi.org/10.26689/ERD.V3I1.2618
  61. Yılmaz, M., Atalar, U., & Topal, E. Ş. (2023). Türkiye’de Dijitalleşme ve Gençlerin İnternet Tabanlı Film-Dizi Platformlarındaki Aile Temsili Algıları: Kahramanmaraş Örneği Digitalization in Türkiye and Youth’s Perceptions of Family Representation on Internet-Based Movie-Series Platforms: The Case of Kahramanmaraş. Toplumsal Politika Dergisi, 4(2), 145–167. https://dergipark.org.tr/tr/pub/tpd
  62. Zhai, Z., Xu, H., & Jia, P. (2010). An empirical study of unsupervised sentiment classification of chinese reviews. Tsinghua Science and Technology, 15(6), 702–708. https://doi.org/10.1016/S1007-0214(10)70118-8
  63. Zhang, Y. (2015). Incorporating phrase-level sentiment analysis on textual reviews for personalized recommendation. WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining, 435–439. https://doi.org/10.1145/2684822.2697033
  64. Barker, C., & Wiatrowski, M. (2017). The age of Netflix: critical essays on streaming media, digital delivery and instant access. McFarland & Company, Inc., Publishers.
  65. Hull, R. (1997). Managing Semantic Heterogeneity in Databases: A Theoretical Perspective. http://arru-db.research.bell-labs.com/user/hull/pods97-tutoridl.html. Introduction to the Syuzhet Package. (n.d.). Retrieved December 30, 2020, from https://cran.r-project.org/web/packages/syuzhet/vignettes/syuzhet-vignette.html?
  66. Lotz, A. D. (2022). Netflix and streaming video: the business of subscriber-funded video on demand. Polity Press.
  67. MacDowell, J. (2014). Happy Endings in Hollywood Cinema: Cliché, Convention and the Final Couple. Edinburgh University Press.