Review on recent advances in information mining from big consumer opinion data for product design

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design

Data trend mining for predictive systems design

The goal of this research is to propose a data mining based design framework that can be used to solve complex systems design problems in a timely and efficient manner, with the main focus being product family design problems. Traditional data acquisition techniques that have been employed in the product design community have relied primarily on customer survey data or focus group feedback as a means of integrating customer preference information into the product design process. The reliance of direct customer interaction can be costly and time consuming and may therefore limit the overall size and complexity of the customer preference data. Furthermore, since survey data typically represents stated customer preferences (customer responses for hypothetical product designs, rather than actual product purchasing decisions made), design engineers may not know the true customer preferences for specific product attributes, a challenge that could ultimately result in misguided product designs. By analyzing large scale time series consumer data, new products can be designed that anticipate emerging product preference trends in the market space. The proposed data trend mining algorithm will enable design engineers to determine how to characterize attributes based on their relevance to the overall product design. A cell phone case study is used to demonstrate product design problems involving new product concept generation and an aerodynamic particle separator case study is presented for product design problems requiring attribute relevance characterization and product family clustering. Finally, it is shown that the proposed trend mining methodology can be expanded beyond product design problems to include systems of systems design problems such as military systems simulations