A framework to discover potential ideas of new product development from crowdsourcing application

In this paper, we study idea mining from crowdsourcing applications which encourage a group of people, who are usually undefined and very large sized, to generate ideas for new product development (NPD). In order to isolate the relatively small number of potential ones among ideas from crowd, decision makers not only have to identify the key textual information representing the ideas, but they also need to consider online opinions of people who gave comments and votes on the ideas. Due to the extremely large size of text data generated by people on the Internet, identifying textual information has been carried out in manual ways, and has been considered very time consuming and costly. To overcome the ineffectiveness, this paper introduces a novel framework that can help decision makers discover ideas having the potential to be used in an NPD process. To achieve this, a semi-automatic text mining technique that retrieves useful text patterns from ideas posted on crowdsourcing application is proposed. Then, we provide an online learning algorithm to evaluate whether the idea is potential or not. Finally to verify the effectiveness of our algorithm, we conducted experiments on the data, which are collected from an existing crowd sourcing website.