Çevrimiçi kullanıcı yorumlarının bilgi temsili için alternatif bir kelime gömme yaklaşımı

Yıl 2023, Cilt: 29 Sayı: 3, 220 - 229, 27.06.2023

Ekin Ekinci Sevinç İlhan Omurca

Öz

E-ticaret alışveriş sitelerinde satın alma kararları, çevrimiçi yorumlardan oldukça etkilenir. Çevrimiçi yorumlar, ürünlere yönelik tercihleri yansıtan ayrıntılı tüketici görüşleri içerse de; önemli bir zorluk, çevrimiçi yorumların miktarının hızlı ve etkili bir analiz için çok büyük olabileceğidir. Bu nedenle, belgelerin tematik yapısını keşfetmek, çevrimiçi yorumları analiz etmede önemli bir rol oynar. Bu çalışmada önerilen sistem, Türk e-ticaret web sitelerindeki çevrimiçi yorumlardaki tüketicilerin ana ilgi alanlarını keşfetmeyi amaçlamaktadır. Bu amaçla, Gizli Dirichlet Ayırımı (GDA) ve word2vec'i birleştiren yeni bir hibrit yöntem önerilmiştir. Son olarak, çalışmamızın performansını, güncel yöntemlerin performansıyla tanınmış Türk e-ticaret sitelerinden toplanan 7 veri kümesi üzerinden karşılaştırdık. Deneysel sonuçlar, önerilen yaklaşımımızın güncel yöntemlere göre önemli ölçüde gelişmiş performans sağlayabildiğini göstermektedir. Ayrıca yöntemimiz, tüketici ilgi alanlarına uygun çok özel konuları keşfetmeyi sağlar.

Anahtar Kelimeler

Müşteri yorumları, Gizli dirichlet ayırımı (GDA), Word2vec, Anlamsal benzerlik, Konu çıkarımı

An alternative word embedding approach for knowledge representation in online consumers’ reviews

Öz

Purchasing decisions in e-commerce shopping websites are highly influenced by online reviews. Although online reviews contain finegrained consumers’ opinions that reflect their preferences towards products; an important challenge, is that the number of online reviews can be very huge for fast and effective analysis. Hence, discovering the thematic structure of documents plays an important role in analyzing online reviews. The proposed system in this paper aims to discover the main consumer interests in online reviews on Turkish e-commerce websites. For this aim, a novel hybrid method combining Latent Dirichlet Allocation (LDA) and word2vec is proposed. Finally, we compare the performance of our work with those of several state-of-theart baselines on 7 datasets collected from well-known Turkish ecommerce websites. The experimental results show how our proposed approach was able to provide significantly improved performance over baselines. Besides, our method enables us to discover very specific topics complying with consumer interests

Anahtar Kelimeler

Consumer reviews, Latent dirichlet allocation (LDA), Word2vec, Semantic similarity, Topic extraction

Kaynakça

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