Restoran Müşteri Yorumlarının Duygu Analizi: Sıfır-Atış Metin Sınıflandırma Yaklaşımı

Year 2025, Volume: 8 Issue: 1, 47 - 62, 18.03.2025

Kutan Koruyan

https://doi.org/10.38016/jista.1543187

Abstract

Bu makale, restoranlara yapılan çevrimiçi müşteri yorumlarından yararlanarak müşteri memnuniyetini değerlendirmek ve artırmak amacıyla makine öğrenmesi ve doğal dil işleme temelli bir yöntem önermektedir. Araştırma, çoğunluğu İzmir Körfezi çevresinde yer alan ilçelerdeki 89 balık restoranına odaklanmakta olup, veri seti 2013-2023 yılları arasında yapılan, 43 farklı dili içeren yaklaşık 15.000 müşteri yorumundan oluşmaktadır. Bu kapsamda, çalışmada hedef tabanlı duygu analizi kullanılarak, yemek kalitesi, servis kalitesi, fiziksel çevre ve adil fiyat restoran kalite boyutları temel alınarak sıfır-atış metin sınıflandırma yöntemiyle müşteri yorumlarının analiz edilmesi amaçlanmaktadır. Model değerlendirme metrikleri ümit verici sonuçlar vermekte olup, her sınıf için %75-%88 arası doğruluk ve %72-%88 arası F1 puanı elde edilmiştir. Önerilen yöntem, restoran yöneticilerinin müşteri yorumlarını otomatik olarak farklı kalite boyutlarında değerlendirmesine, restoranın güçlü ve zayıf yönlerini belirlemesine, zaman içinde müşteri memnuniyetindeki değişimleri izlemesine, rakip restoranlarla performans karşılaştırması yapmasına ve Türkçe ile yabancı dildeki müşteri yorumlarını birlikte veya ayrı ayrı analiz etmesine olanak tanımaktadır. Çalışmada önerilen bu yaklaşım, restoran yöneticilerine müşteri beklentilerini daha derinlemesine anlama ve restoran kalitesini iyileştirme konusunda veri analizi odaklı bir yol haritası sunmaktadır.

Keywords

Sıfır-Atış Metin Sınıflandırma, Hedef Tabanlı Duygu Analizi, Müşteri Yorumları, Müşteri Memnuniyeti, Çok Dilli Veri Seti

Sentiment Analysis of Restaurant Customer Reviews: A Zero-Shot Text Classification Approach

Abstract

This paper proposes a machine learning and natural language processing-based method to evaluate and increase customer satisfaction by using online customer reviews of restaurants. The research focuses on 89 fish restaurants, mostly located in the districts around Gulf of Izmir, and the dataset consists of approximately 15,000 customer reviews written between 2013 and 2023, covering 43 different languages. In this context, the study aims to analyse customer reviews using target-based sentiment analysis using zero-shot text classification method based on restaurant quality dimensions of food quality, service quality, physical environment, and fair price. Model evaluation metrics give promising results, with accuracy between 75% and 88% and F1 score between 72% and 88% for each class. The proposed method allows restaurant managers to automatically evaluate customer reviews on different quality dimensions, identify restaurant strengths and weaknesses, monitor changes in customer satisfaction over time, compare performance with competitor restaurants, and analyse Turkish and foreign language customer reviews together or separately. This approach proposed in the study provides restaurant managers with a data analysis-focused roadmap to understand customer expectations more deeply and improve restaurant quality.

Keywords

Zero-Shot Text Classification, Target-Based Sentiment Analysis, Customer Reviews, Customer Satisfaction, Multilingual Data Set

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