Etkileyici Pazarlamada Metin Madenciliği: YouTube Tüketici Yorumlarının Duygu Analizinde MAXQDA ve Orange Yazılımlarının Karşılaştırmalı Performansı

Öz

Dijitalleşen pazarlama dünyasında, etkileyici (influencer) pazarlama stratejileri kapsamında üretilen video içerikleri ve bu içeriklere gelen kullanıcı geri bildirimleri, büyük veri (big data) analitiği için kritik bir zemin oluşturmaktadır. Sosyal medya mecralarında üretilen yüksek hacimli ve dinamik verinin manuel yöntemlerle işlenmesi imkansız hale geldiğinden, yapay zeka destekli metin madenciliği yöntemlerine olan ihtiyaç artmıştır. Bu çalışma, YouTube üzerindeki bir ürün tanıtım videosuna yönelik kullanıcı tepkilerini duygu analizi (sentiment analysis) yöntemiyle incelemeyi ve bu süreçte kullanılan farklı yazılımların doğruluk performanslarını karşılaştırmayı amaçlamaktadır. Araştırma kapsamında, bir içerik üreticisinin VR gözlük tanıtım videosu altına gelen 842 adet kullanıcı yorumu; nitel veri analiz programı MAXQDA ve veri madenciliği yazılımı Orange 3 kullanılarak analiz edilmiştir. Elde edilen veriler; olumlu, olumsuz ve nötr duyguların sınıflandırılmasındaki tutarlılık ve algoritmik farklılıklar açısından değerlendirilmiştir. Bulgular, her iki yazılımın da duygu sınıflandırmasında genel olarak uyumlu sonuçlar verdiğini göstermekle birlikte, bazı nüanslarda farklılaştıklarını ortaya koymaktadır

Anahtar Kelimeler

• etkileyici pazarlama
• duygu analizi
• metin madenciliği
• maxqda
• orange 3 veri madenciliği aracı

Text Mining in Influencer Marketing: A Comparative Analysis of the Performance of MAXQDA and Orange Software in Sentiment Analysis of YouTube Consumer Comments

Öz

In the digital marketing landscape, video content produced as part of influencer marketing strategies and the user feedback received on this content form a critical foundation for big data analytics. As the high-volume and dynamic data generated on social media platforms has become impossible to process manually, the need for AI-powered text mining methods has increased. This study aims to examine user reactions to a product promotional video on YouTube using sentiment analysis and to compare the accuracy performance of different software used in this process. Within the scope of the research, 842 user comments posted under a content creator’s VR headset promotional video were analyzed using the qualitative data analysis program MAXQDA and the data mining software Orange 3. The obtained data were evaluated in terms of consistency and algorithmic differences in the classification of positive, negative, and neutral sentiments. The findings indicate that while both software programs generally produce consistent results in sentiment classification, they differ in certain nuances.

Anahtar Kelimeler

• influencer marketing
• sentiment analysis
• text mining
• maxqda,
• orange 3 data mining

Kaynakça

1. Abidin, C. (2015). “Communicative intimacies: In ouencers and perceived interconnectedness“, A journal of Gender, New Media, and Technology, 8: 1-16.
2. Acikgoz, F., & Burnaz, S. (2021). “The influence of influencer marketing on YouTube influencers”, International Journal of Internet Marketing and Advertising, 15(2): 201-219.
3. Aggarwal, C. C. (2011). An Introduction to Social Network Data Analytics. In Social Network Data Analytics. (Pp. 1-15). Boston, MA: Springer US.
4. Ahn, R. J., Cho, S. Y. & Sunny Tsai, W. (2022). “Demystifying computer-generated imagery (CGI) Influencers: The effect of perceived anthropomorphism and social presence on brand outcomes”, Journal of Interactive Advertising, 22(3): 327-335.
5. Carter, D. (2016). “Hustle and brand: The sociotechnical shaping of influencer”, Social Media and Society, 2(3): 1-12.
6. Çeliktuğ, M. F. (2018). “Twitter sentiment analysis, 3-Way Classification: positive, negative or neutral?”, In 2018 IEEE International Conference on Big Data (big data) (pp. 2098-2103), December.
7. Da Silva Oliveira, A. B. V& Chimenti, P. (2021). " Humanized robots: A proposition of categories to understand virtual influencers”. Australasian Journal of Information Systems, Vol 25: 1-27.
8. Dinçer, C. (2022). İçerik Pazarlama. M. Baş, & İE Tarakçı içinde, Pazarlama: Teoriden Pratiğe: 7-26.

9. Duffett, R., Petroșanu, D. M., Negricea, I. C., & Edu, T. (2019).” Effect of YouTube marketing communication on converting brand liking into preference among millennials regarding brands in general and sustainable offers in particular evidence from South Africa and Romania”, Sustainability, 11(3): 604.
10. Feldman, R. (2013). “Techniques and Applications for Sentiment Analysis”. Communications of the ACM, 56(4), 82-89.
11. Goldsmith, R.E., ve R.A. Clark ( 2008). “An analysis of factors affecting fashion opinion leadership and fashion opinion seeking”, Journal of Fashion Marketing and Management 12, no. 3: 308–22.
12. Hartmann, J., Huppertz, J., Schamp, C., & Heitmann, M. (2019). “Comparing automated text classification methods”, International Journal of Research in Marketing, 36(1): 20-38.
13. Ibrahim, N. F., Wang, X., & Bourne, H. (2017). “Exploring the effect of user engagement in online brand communities: Evidence from Twitter”, Computers in Human Behavior, 72: 321-338.
14. Karaman, Ö. (2023). “Influencer marketing on YouTube: A qualitative analysis with MAXQDA”, Journal of Business Administration and Social Studies, 7(2): 123-132.
15. Lee, C.S. (2012), “Exploring emotional expressions on Youtube through the lens of media system dependency theory”, New Media Society, 14 (3), 457-475.
16. Liu, B. (2012) Sentiment Analysis and Opinion Mining: Morgan and Claypool Salas-Zárate, M. D. P., Medina-Moreira, J., Lagos-Ortiz, K., Luna-Aveiga, H., Rodriguez-Garcia, M. A. & Valencia-
17. Garcia, R. (2017). “Sentiment analysis on tweets about diabetes: An aspect-level approach”. Computational and Mathematical Methods in Medicine: 1-9.
18. Mrad, M., Ramadan, Z. & Nasr, L. I. (2022). “Computer-Generated influencers: The Rise of Digital Personalities”, Marketing Intelligence & Planning, 40(5), 589-603.
19. Nasukawa, T., & Yi, J. (2003, October).” Sentiment Analysis: Capturing Favorability Using Natural Language Processing”, In Proceedings of the 2nd International Conference on Knowledge Capture (pp. 70-77).
20. Peng, S., Cao, L., Zhou, Y., Ouyang, Z., Yang, A., Li, X., ... & Yu, S. (2022). “A survey on deep learning for textual emotion analysis in social networks”, Digital Communications and Networks, 8(5): 745-762.
21. Srinivasan, S., Rutz, O. J., & Pauwels, K. (2016). Paths to and off purchase: quantifying the impact of traditional marketing and online consumer activity. Journal of the academy of marketing science, 44(4), 440-453.
22. Soleymani, M., Garcia, D., Jou, B., Schuller, B., Chang, S. F., & Pantic, M. (2017). A survey of multimodal sentiment analysis. Image and vision computing, 65, 3-14.
23. Thorne, L. (2008). Word-Of-Mouth Advertising, Online and Off: How to Spark Buzz, Excitement, And Free Publicity For Your Business Or Organization With Little Or No Money, : Atlantic Publishing Company, Florida.
24. https://www.youtube.com/watch?v=4ESPrg71FLo (14.04.2024)
25. https://www.maxqda.com/ ( 10.09.2025)
26. https://orangedatamining.com/ (10.09.2025)
27. https://orangedatamining.com/home/visual-programming/ (13.10.2025)