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Journals / Erzurum Teknik Üniversitesi Sosyal Bilimler Enstitüsü Dergisi / 2025 / Sayı: 23

Sentiment Analysis of Online Customer Reviews Using Modern Natural Language Processing Approaches

Modern Doğal Dil İşleme Yaklaşımları Kullanarak Çevrimiçi Müşteri Yorumlarının Duygu Analizi

Kutan Koruyan(DOKUZ EYLÜL ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ, YÖNETİM BİLİŞİM SİSTEMLERİ BÖLÜMÜ)0000-0002-3115-5676
Pages
22–40
Publication Date
2025-09-30
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AI Summary & Key Takeaways

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

The findings indicate that LLMs—particularly DeepSeek V3—demonstrated high performance and could effectively process contextual representations even in unlabelled datasets.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, sentiment analysis was conducted using Turkish online customer reviews of four seafood restaurants based in İzmir.
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Key Findings & Data:In addition, large language models (LLMs) such as OpenAI 4o, Gemini 2.0 Flash, and DeepSeek V3 were evaluated.
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Conclusion & Impact:The findings indicate that LLMs—particularly DeepSeek V3—demonstrated high performance and could effectively process contextual representations even in unlabelled datasets.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Duygu Analizi · BERT · Sıfır Atış Metin Sınıflandırma

Abstract

In this study, sentiment analysis was conducted using Turkish online customer reviews of four seafood restaurants based in İzmir. Modern natural language processing approaches were employed as part of the analysis, including a Turkish sentiment analysis model for BERT and multilingual models within the framework of zero-shot text classification. In addition, large language models (LLMs) such as OpenAI 4o, Gemini 2.0 Flash, and DeepSeek V3 were evaluated. Model performance was assessed using evaluation metrics, including accuracy, precision, recall, and F1 score. The findings indicate that LLMs—particularly DeepSeek V3—demonstrated high performance and could effectively process contextual representations even in unlabelled datasets. Furthermore, sentiment trends towards restaurants over a four years were analysed to track temporal changes in customer satisfaction. This approach revealed temporal performance differences among the restaurants over time and enabled the development of sustainable improvement strategies aligned with customer expectations. The proposed method offers a fast and data-driven solution to assist managers in monitoring customer satisfaction, evaluating service quality, and identifying underlying causes of dissatisfaction, supporting strategic decision-making processes and contributing to corporate image management.

Özet

Bu çalışmada, İzmir’de faaliyet gösteren dört deniz ürünleri restoranına yapılan Türkçe çevrimiçi müşteri yorumları kullanılarak duygu analizi gerçekleştirilmiştir. Analiz sürecinde modern doğal dil işleme yaklaşımlarından yararlanılmış; bunlar arasında BERT için kullanılan bir Türkçe duygu analizi modeli ile sıfır atışlı metin sınıflandırma yöntemi kapsamında kullanılan çok dilli modeller yer almaktadır. Bunlara ek olarak, OpenAI 4o, Gemini 2.0 Flash ve DeepSeek V3 gibi Büyük Dil Modelleri (BDM) de değerlendirmeye dahil edilmiştir. Model performansları doğruluk, kesinlik, duyarlılık ve F1 puanı metrikleri aracılığıyla ölçülmüştür. Elde edilen bulgular, özellikle DeepSeek V3 başta olmak üzere BDM’lerin yüksek performans sergilediğini ve bağlamsal temsilleri etiketlenmemiş veri kümelerinde dahi etkili biçimde işleyebildiğini ortaya koymaktadır. Ayrıca müşteri memnuniyetindeki zamansal değişimleri izleyebilmek amacıyla restoranlara yönelik dört yıllık bir dönemdeki duygu eğilimleri analiz edilmiştir. Bu yaklaşım sayesinde restoranların zaman içerisindeki performans farklılıkları ortaya konmakta ve müşteri beklentilerine yönelik sürdürülebilir iyileştirme stratejilerinin geliştirilmesine olanak tanınmaktadır. Önerilen yöntem, yöneticilerin müşteri memnuniyetini izlemeleri, hizmet kalitesini değerlendirmeleri ve olası memnuniyetsizlik nedenlerini tespit etmeleri açısından hızlı ve veri odaklı bir çözüm sunmakta; böylece stratejik karar alma süreçlerini desteklemekte ve kurumsal imaj yönetimine katkı sağlamaktadır.

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Cite This Article

Koruyan, K. (2025). Sentiment Analysis of Online Customer Reviews Using Modern Natural Language Processing Approaches. Erzurum Teknik Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Sayı: 23, 22–40. https://doi.org/10.29157/etusbed.1711500

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