RESUMO
This study investigates customers' product design requirements through online comments from social media, and quickly translates these needs into product design specifications. First, the exponential discriminative snowball sampling method was proposed to generate a product-related subnetwork. Second, natural language processing (NLP) was utilized to mine user-generated comments, and a Graph SAmple and aggreGatE method was employed to embed the user's node neighborhood information in the network to jointly define a user's persona. Clustering was used for market and product model segmentation. Finally, a deep learning bidirectional long short-term memory with conditional random fields framework was introduced for opinion mining. A comment frequency-invert group frequency indicator was proposed to quantify all user groups' positive and negative opinions for various specifications of different product functions. A case study of smartphone design analysis is presented with data from a large Chinese online community called Baidu Tieba. Eleven layers of social relationships were snowball sampled, with 14,018 users and 30,803 comments. The proposed method produced a more reasonable user group clustering result than the conventional method. With our approach, user groups' dominating likes and dislikes for specifications could be immediately identified, and the similar and different preferences of product features by different user groups were instantly revealed. Managerial and engineering insights were also discussed.
