The online customer reviews provide important information for product improvement and redesign. However, many reviews are redundant with only several short sentences, which may even conflict with each other on the same aspect of a product. Thus it is usually a very challenging task to extract useful design information from the reviews and provide a clear description on the product’s various aspects amongst its competitors. In order to resolve this issue, we propose an approach to build hierarchical product profiles to describe a product’s kernel design aspects quantitatively. It is achieved via three main strategies: a double propagation strategy to achieve the associated features and customers’ descriptions; a deep text processing network to build the aspect hierarchy; an aspect ranking approach to quantify each kernel design aspect. Experimental results validate the effectiveness of the proposed approach on online reviews. 

关键词： customer reviews,  quantitative product profile,  product aspect ranking,  deep learning