Extraction and prioritization of product attributes using an explainable neural network
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INDUSTRIAL AND COMMERCIAL APPLICATION
Extraction and prioritization of product attributes using an explainable neural network Younghoon Lee1 · Jungmin Park2 · Sungzoon Cho3 Received: 5 January 2019 / Accepted: 30 March 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Identification of product attributes is an important matter in real-world business environments because customers generally make purchase decisions based on their evaluation of the attributes of the product. Numerous studies on product attribute extraction have been performed on the basis of user-generated textual reviews. However, most of them focused only on the attribute extraction process itself and not on the relative importance of the extracted attributes, which are critical information that can be utilized for the promotion or development of specification sheets. Thus, in this study, we focused on the development of an attribute set for a product by considering the relative importance of the extracted attributes. First, we extracted the aspects by utilizing convolutional neural network-based approaches and transfer learning. Second, we propose a novel approach, consisting of variants of the Gradient-weighted class activation mapping (Grad-CAM) algorithm, one of the explainable neural network frameworks, to capture the importance score of each extracted aspect. Using a sentimental prediction model, we calculated the weight of each aspect that affects the sentiment decision. We verified the performance of our proposed method by comparing the similarity of the product attributes that it extracted and their relative importance with the product attributes that customers consider to be the most important and by comparing the attributes used to develop the specification sheet of an existing major commercial site. Keywords Attribute extraction · Attribute prioritization · Grad-CAM · Explainable neural network · Convolutional neural network · Transfer learning
1 Introduction
* Sungzoon Cho [email protected] Younghoon Lee [email protected] Jungmin Park [email protected] 1
Department of Industrial and Systems Engineering, Seoul National University of Science and Technology, 232 Gongneung‑ro, Nowon‑gu, Seoul 01811, Korea
2
Idea Lab, Corporate Business Strategy Office, LG Electronics, 128 Yeouidaero,Yeongdeungpo‑gu, Seoul 07336, Korea
3
Department of Industrial Engineering, Institute for Industrial Systems Innovation, Seoul National University, 1 Gwanak‑ro, Gwanak‑gu, Seoul 08826, Korea
Customers generally make purchase decisions based on their evaluation and knowledge of the attributes of the product of interest [8, 29]. Thus, product developers or marketers are frequently interested in identifying the product attributes that customers consider to be most important when they evaluate and/or purchase a product [3]. The important product attributes that have been identified can then be used for product promotion. Another example is the specification sheet (Fig. 4), which is a list that describes the specifica
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