Data scientists working on text analytics know cleaning data can be time consuming. Users of DiscoverText build reusable custom machine classifiers or "sifters" to find the most (or least) relevant items before using other classifiers for sorting items into topic, sentiment, and other categories. DiscoverText combines hybrid data science methods (measurement, adjudication, iteration, replication) along with established e-discovery text analytics tools, to shorten a process that used to last weeks or months when words get sorted in spreadsheets. Our machine-learning sifters are created in hours or just a few minutes using crowdsourcing. We offer an API and support technical integrations with Twitter and SurveyMonkey.