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Community smells appear in sub-optimal software development communitystructures, causing unforeseen additional project costs, e.g., lowerproductivity and more technical debt. Previous studies analyzed and predictedcommunity smells in the granularity of community sub-groups usingsocio-technical factors. However, refactoring such smells requires the effortof developers individually. To eliminate them, supportive measures for everydeveloper should be constructed according to their motifs and working states.Recent work revealed developers' personalities could influence communitysmells' 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-awarecommunity smell prediction model considering 3 smells such as OrganizationalSilo, Lone Wolf, and Bottleneck. Furthermore, it also predicts if a developerquitted the community after being affected by any smell. The proposed modelachieves cross- and within-project prediction F-Measure ranging from 76% to93%. Research also reveals 6 sentimental features having stronger predictivepower compared with activeness metrics. Imperative and indicative expressions,politeness, and several emotions are the most powerful predictors. Finally, wetest statistically the mean and distribution of sentimental features. Based onour findings, we suggest developers should communicate in a straightforward andpolite way.

Predicting Community Smells' Occurrence on Individual Developers by Sentiments