The role of sentiment analysis is increasingly emerging to study software developers' emotions by mining crowd-generated content within software repositories and information sources. With a few notable exceptions [1][5], empirical software engineering studies have exploited off-the-shelf sentiment analysis tools. However, such tools have been trained on non-technical domains and general-purpose social media, thus resulting in misclassifications of technical jargon and problem reports [2][4]. In particular, Jongeling et al. [2] show how the choice of the sentiment analysis tool may impact the conclusion validity of empirical studies because not only these tools do not agree with human annotation of developers' communication channels, but they also disagree among themselves.

Crowd creativity is typically associated with peer-production communities focusing on artistic products like animations, video games, and music, but less frequently to Open Source Software (OSS), despite the fact that also developers must be creative to come up with new solutions to their technical challenges. In this paper, we conduct a study to further the understanding of which factors from prior work in both OSS and art communities are predictive of successful collaboration - defined as reuse of previous songs - in three different songwriting communities, namely Songtree, Splice, and ccMixter. The main findings from this study confirm that the success of collaborations is associated with high community status of recognizable authors and low degree of derivativity of songs.