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Journals / Gazi University Journal of Science Part B: Art Humanities Design and Planning / 2026 / Cilt: 14 Sayı: 1

Empirical Identification of Online Consumer Reviewer Archetypes: A Text Mining Approach to Understanding Motivations Through Self-Determination Theory

Çevrimiçi Tüketici Yorumcularının Arketiplerinin Ampirik Tanımlanması: Öz Belirleme Teorisiyle Motivasyonları Anlamak için Metin Madenciliği Yaklaşımı

Serkan Güneş(GAZİ ÜNİVERSİTESİ)0000-0003-4377-528X
Çisem Ercömert(BİLECİK ŞEYH EDEBALİ ÜNİVERSİTESİ)0000-0001-7588-024X
Pages
79–109
Publication Date
2026-03-31
DOI
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AI Summary & Key Takeaways

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

Findings reveal that reviewer motivations align with SDT principles, offering actionable insights for businesses to tailor marketing strategies effectively for diverse consumer segments.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This research integrates text mining and prediction, rooted in Self-Determination Theory, to analyze online consumer reviewer archetypes and motivations.
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Key Findings & Data:Examining 32,534 reviews and 480 past studies, it employs Word2Vec and SpaCy to classify reviewers into monomyth archetypes, uncovering the "why" behind their contributions.
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Conclusion & Impact:Findings reveal that reviewer motivations align with SDT principles, offering actionable insights for businesses to tailor marketing strategies effectively for diverse consumer segments.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Çevrimiçi Müşteri Yorumları · Özbelirleme Teorisi · Motivasyon

Abstract

This research integrates text mining and prediction, rooted in Self-Determination Theory, to analyze online consumer reviewer archetypes and motivations. Examining 32,534 reviews and 480 past studies, it employs Word2Vec and SpaCy to classify reviewers into monomyth archetypes, uncovering the "why" behind their contributions. The Python script accurately predicts and categorizes reviewers, linking archetypes to SDT principles and distinguishing autonomous from controlled motivations. It identifies SDT domains, motivation types, and subtypes tied to each archetype while defining their behavioral patterns from literature. Findings reveal that reviewer motivations align with SDT principles, offering actionable insights for businesses to tailor marketing strategies effectively for diverse consumer segments. The study highlights the effectiveness of combining Word2Vec and SpaCy for precise archetypal classification, establishing a robust methodology for future studies in consumer behavior and motivation-driven marketing literature. This nuanced approach underscores the potential of big data analytics to deepen understanding of consumer behavior and motivations, advancing marketing research.

Özet

Bu araştırma, çevrimiçi tüketici yorumcularının arketiplerini ve motivasyonlarını analiz etmek için, Öz Belirleme Teorisi'ne dayanan metin madenciliği ve tahmin yöntemlerini birleştirir. 32.534 yorum ve 480 geçmiş çalışmayı inceleyen araştırma, Word2Vec ve SpaCy kullanarak yorumcuları tek mit arketiplerine sınıflandırır ve katkılarının ardındaki “neden”i ortaya çıkarır. Python komut dosyası, yorumcuları doğru bir şekilde tahmin edip sınıflandırarak, arketipleri SDT ilkeleriyle ilişkilendirir ve özerk motivasyonları kontrollü motivasyonlardan ayırır. Literatürden davranış kalıplarını tanımlarken, her arketiple bağlantılı SDT alanlarını, motivasyon türlerini ve alt türlerini belirler. Bulgular, yorumcuların motivasyonlarının SDT ilkeleriyle uyumlu olduğunu ortaya koyarak, işletmelere çeşitli tüketici segmentleri için pazarlama stratejilerini etkili bir şekilde uyarlayabilmeleri için eyleme geçirilebilir içgörüler sunar. Çalışma, kesin arketip sınıflandırması için Word2Vec ve SpaCy'yi birleştirmenin etkinliğini vurgular ve tüketici davranışı ve motivasyon odaklı pazarlama literatüründe gelecekteki çalışmalar için sağlam bir metodoloji oluşturur. Bu incelikli yaklaşım, tüketici davranışını ve motivasyonlarını daha derinlemesine anlamak ve pazarlama araştırmalarını ilerletmek için büyük veri analizinin potansiyelini vurgular.

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Cite This Article

Güneş, S., Ercömert, Ç. (2026). Empirical Identification of Online Consumer Reviewer Archetypes: A Text Mining Approach to Understanding Motivations Through Self-Determination Theory. Gazi University Journal of Science Part B: Art Humanities Design and Planning, Cilt: 14 Sayı: 1, 79–109. https://ajindex.com/articles/1486761

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