Ranking products through online reviews: A novel data-driven method based on interval type-2 fuzzy sets and sentiment analysis

Küçük Resim Yok

Tarih

2024

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Taylor & Francis Ltd

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

As an essential information resource, online reviews play an important role in consumers' decision-making processes. To solve the product ranking problem through online reviews, two important issues are involved: sentiment analysis (SA) for online reviews and product ranking based on multi-criteria decision-making (MCDM) methods. However, merely a few studies have considered the impact of SA accuracy, which can significantly affect the final decision-making process. This paper proposes a novel data-driven method for ranking products through online reviews based on interval type-2 fuzzy sets (IT2FSs) and SA. In this method, after acquiring online reviews, the explicit and implicit attributes are extracted from the website itself and the latent Dirichlet allocation (LDA) model, respectively. Thereafter, a deep learning model is adopted to identify the five sentiment intensities of online reviews, based on which the SA results are represented as IT2FSs by considering the classification effect. After type-reduction for IT2FSs, the ranking order is obtained based on the exponential TODIM (ExpTODIM) method. Furthermore, a case study on ranking travel products from Trip.com Group through online reviews is provided to illustrate the effectiveness and applicability of the proposed method.

Açıklama

Anahtar Kelimeler

Product Ranking, Online Reviews, Sentiment Analysis, Interval Type-2 Fuzzy Sets, Exptodim

Kaynak

Journal of the Operational Research Society

WoS Q Değeri

N/A

Scopus Q Değeri

Q1

Cilt

75

Sayı

5

Künye