1. A method, comprising: generating, by a machine-based sentiment prediction generator, respective machine-determined sentiment predictions for each of a plurality of software patches using sentiment analysis; setting, by a sentiment-based confidence value generator, a confidence value for each of the plurality of software patches based on the machine-determined sentiment predictions; adjusting the machine-determined sentiment predictions based on learned information in response to an alert triggered based on predetermined criteria to generate one or more subsequent machine-determined sentiment predictions; and at least one of selecting and prioritizing, by a software patch selector and prioritizer, at least one of the plurality of software patches based on the respective confidence value therefor, wherein the sentiment analysis comprises: building a sentiment analysis graph, where graph nodes represent at least one of respective users and respective software patches, and edges indicate sentiment between the respective users and the respective software patches; and clustering the respective users into one of at least three clusters that include a positive sentiment cluster, a negative sentiment cluster, and a neutral sentiment cluster.