Micro-blog forums, such as Twitter, constitute a powerful medium today that people use to express their thoughts and intentions on a daily, and in many cases, hourly, basis. Extracting 'Real-Time Intention' (RTI) of a user from such short text updates is a huge opportunity towards web personalization and social networking around dynamic user context. In this paper, we explore the novel problem of detecting and classifying RTIs from micro-blogs. We find that employing a heuristic based ensemble approach on a reduced dimension of the feature space, based on a wide spectrum of linguistic and statistical features of RTI expressions, achieves significant improvement in detecting RTIs compared to word-level features used in many social media classification tasks today. Our solution approach takes into account various salient characteristics of micro-blogs towards such classification - high dimensionality, sparseness of data, limited context, grammatical in-correctness, etc Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.