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.

中文翻译：

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使用可解释的神经网络提取和确定产品属性的优先级

在现实世界的业务环境中，产品属性的标识是一件重要的事情，因为客户通常根据对产品属性的评估来做出购买决定。基于用户生成的文本评论，对产品属性提取进行了大量研究。但是，它们中的大多数仅集中于属性提取过程本身，而没有集中于所提取属性的相对重要性，而提取属性是可以用于规范表的升级或开发的关键信息。因此，在这项研究中，我们通过考虑提取的属性的相对重要性，专注于产品属性集的开发。首先，我们利用基于卷积神经网络的方法和转移学习来提取方面。第二，我们提出了一种新颖的方法，该方法由可解释的神经网络框架之一的梯度加权类激活映射（Grad-CAM）算法的变体组成，以捕获每个提取方面的重要性得分。使用情感预测模型，我们计算了影响情感决策的各个方面的权重。我们通过比较提取的产品属性的相似性及其相对重要性与客户认为最重要的产品属性，以及通过比较用于开发现有专业规格表的属性，验证了所提出方法的性能商业站点。捕获每个提取方面的重要性得分。使用情感预测模型，我们计算了影响情感决策的各个方面的权重。我们通过比较提取的产品属性的相似性及其相对重要性与客户认为最重要的产品属性，以及通过比较用于开发现有专业规格表的属性，验证了所提出方法的性能商业站点。捕获每个提取方面的重要性得分。使用情感预测模型，我们计算了影响情感决策的各个方面的权重。我们通过比较提取的产品属性的相似性及其相对重要性与客户认为最重要的产品属性，以及通过比较用于开发现有专业规格表的属性，验证了所提出方法的性能商业站点。