LAUSR

Abstract One of the main challenges of open innovation communities is how to create value from shared content either by selecting those ideas that are worthy of pursuit and implementation or by identifying the users' preferences and needs. These tasks can be done manually when there is an overseeable amount of content or by using computational tools when there are massive amounts of data. However, previous studies on text mining have not dealt with the identification of unique attributes, which can be defined as those contributions that are inextricably linked with a specific tag or category within open innovation websites. The uniqueness of these ideas means that they can only be obtained through a selection of one choice among several alternatives. To obtain such unique ideas and thus to also obtain innovations, this paper proposes a novel methodology called co-occurrence differential analysis. The proposed methodology combines traditional co-occurrence analysis with additional statistical processing to obtain the unique attributes and topics associated with different alternatives. The identification of unique content provides valuable information that can reveal the strengths and weaknesses of several options in a comparative fashion.