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

Authors : Ekin EKİNCİ, Sevinç İLHAN OMURCA

Pages : 220-229

View : 22 | Download : 26

Publication Date : 2023-06-27

Article Type : Research Paper

Abstract :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 insert ignore into journalissuearticles values(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
Keywords : Müşteri yorumları, Gizli dirichlet ayırımı GDA, , Word2vec, Anlamsal benzerlik, Konu çıkarımı