  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.
