  Community smells appear in sub-optimal software development community
structures, causing unforeseen additional project costs, e.g., lower
productivity and more technical debt. Previous studies analyzed and predicted
community smells in the granularity of community sub-groups using
socio-technical factors. However, refactoring such smells requires the effort
of developers individually. To eliminate them, supportive measures for every
developer should be constructed according to their motifs and working states.
Recent work revealed developers' personalities could influence community
smells' variation, and their sentiments could impact productivity. Thus,
sentiments could be evaluated to predict community smells' occurrence on them.
To this aim, this paper builds a developer-oriented and sentiment-aware
community smell prediction model considering 3 smells such as Organizational
Silo, Lone Wolf, and Bottleneck. Furthermore, it also predicts if a developer
quitted the community after being affected by any smell. The proposed model
achieves cross- and within-project prediction F-Measure ranging from 76% to
93%. Research also reveals 6 sentimental features having stronger predictive
power compared with activeness metrics. Imperative and indicative expressions,
politeness, and several emotions are the most powerful predictors. Finally, we
test statistically the mean and distribution of sentimental features. Based on
our findings, we suggest developers should communicate in a straightforward and
polite way.
