Baseline ve Hibrit Yaklaşımlarını Kullanarak E-Ticaret Sitelerindeki Ürün Yorumlarının Özetlenmesi

Öz

E-ticaret platformlarında kullanıcılar tarafından üretilen ürün yorumları, satın alma kararlarını etkileyen önemli bir bilgi kaynağıdır. Ancak yüz binlerce yorumu tek tek incelemek hem kullanıcılar hem de işletmeler açısından pratik değildir. Bu çalışmada, Türkçe e-ticaret ürün yorumlarının otomatik özetlenmesi problemi ele alınmış ve çıkarımsal (extractive) bir temel yöntem ile aspect-temelli hibrit bir özetleme yaklaşımı karşılaştırmalı olarak incelenmiştir. Çalışmada kullanılan veri seti, Doğukan Sürel tarafından Trendyol platformundan toplanmış 476 farklı ürüne ait 582.601 kullanıcı yorumundan oluşmaktadır. Baseline yöntemde TF-IDF centroid tabanlı çıkarımsal özetleme uygulanırken, hibrit yaklaşımda ürün yorumlarından otomatik olarak çıkarılan aspect’ler kullanılarak yapılandırılmış özetler üretilmiştir. Yöntemler; ROUGE-1, ROUGE-2 ve ROUGE-L F1 metrikleri, aspect kapsama oranı, token tekrar oranı ve özet uzunluğu gibi ölçütler üzerinden değerlendirilmiştir. Deneysel sonuçlar, hibrit yaklaşımın ROUGE metriklerinde daha düşük skorlar üretmesine karşın, aspect kapsama oranı ve tekrar oranı açısından belirgin üstünlük sağladığını göstermektedir. Elde edilen bulgular, aspect-temelli yapılandırılmış özetlemenin e-ticaret yorumları için daha açıklayıcı ve kapsayıcı özetler sunduğunu ortaya koymaktadır.

Anahtar Kelimeler

• Ürün Yorumu Özetleme
• Hibrit Özetleme Yaklaşımı
• Aspect Tabanlı Özetleme
• Çıkarımsal Özetleme

Summarizing Product Reviews on E-Commerce Platforms Using Baseline and Hybrid Approaches

Öz

User-generated product reviews on e-commerce platforms constitute an important source of information that influences purchasing decisions. However, examining hundreds of thousands of reviews individually is impractical for both users and businesses. In this study, the problem of automatic summarization of Turkish e-commerce product reviews is addressed, and an extractive baseline method is comparatively analyzed with an aspect-based hybrid summarization approach. The dataset used in this study consists of 582,601 user reviews belonging to 476 different products collected from the Trendyol platform by Doğukan Sürel. In the baseline approach, a TF-IDF centroid-based extractive summarization method is applied, whereas the hybrid approach generates structured summaries by utilizing automatically extracted aspects from product reviews. The methods are evaluated using ROUGE-1, ROUGE-2, and ROUGE-L F1 metrics, as well as aspect coverage ratio, token repetition ratio, and summary length. Experimental results show that although the hybrid approach produces lower scores on ROUGE metrics, it achieves significant improvements in terms of aspect coverage and repetition reduction. These findings indicate that aspect-based structured summarization provides more informative, comprehensive, and user-oriented summaries for e-commerce product reviews

Anahtar Kelimeler

• Product Review Summarization
• Hybrid Summarization Approach
• Aspect-Based Summarization
• Extractive Summarization

Kaynakça

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