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Journals / Selçuk Üniversitesi Akşehir Meslek Yüksekokulu Sosyal Bilimler Dergisi / 2026 / Sayı: 21

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

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

Güneş Açelya Sipahi(AYDIN ADNAN MENDERES ÜNİVERSİTESİ)0000-0002-6414-5618
Pages
100–110
Publication Date
2026-06-30
DOI
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AI Summary & Key Takeaways

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

The findings indicate that while both software programs generally produce consistent results in sentiment classification, they differ in certain nuances.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact:The findings indicate that while both software programs generally produce consistent results in sentiment classification, they differ in certain nuances.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:etkileyici pazarlama · duygu analizi · metin madenciliği

Abstract

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.

Özet

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

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Cite This Article

Sipahi, G. A. (2026). Text Mining in Influencer Marketing: A Comparative Analysis of the Performance of MAXQDA and Orange Software in Sentiment Analysis of YouTube Consumer Comments. Selçuk Üniversitesi Akşehir Meslek Yüksekokulu Sosyal Bilimler Dergisi, Sayı: 21, 100–110. https://ajindex.com/articles/1620881

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