Modern Doğal Dil İşleme Yaklaşımları Kullanarak Çevrimiçi Müşteri Yorumlarının Duygu Analizi

Abstract

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.

Keywords

• Duygu Analizi
• BERT
• Sıfır Atış Metin Sınıflandırma
• Büyük Dil Modelleri
• Doğal Dil İşleme

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

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.

Keywords

• Sentiment Analysis
• BERT
• Zero-Shot Text Classification
• Large Language Models
• Natural Language Processing

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