Many consumer brands have customer relationship agents that directly engage opinionated consumers on social streams, such as Twitter. To help agents find opinionated consumers, social stream monitoring tools provide keyword-based filters, which are often too coarse-grained to be effective. In this work, we introduce CrowdE, a Twitter-based filtering system that helps agents find opinionated customers through brand-specific intelligent filters. To minimize per-brand effort in creating these brand-specific filters, the system used a common crowd-enabled process that creates the filters through machine learning over crowd-labeled tweets. We validated the quality of the crowd labels and the performance of the filter algorithms built from the labels. A user evaluation further showed that CrowdE's intelligent filters improved task performance and were generally preferred by users in comparison to keyword-based filters in current social stream monitoring tools. Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.