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 propose novel ensemble approaches for learning and classifying RTI expressions from micro-blogs, based on a wide spectrum of linguistic and statistical features of RTI expressions (viz. high dimensionality, sparseness of data, limited context, grammatical in-correctness, etc.). We demonstrate our approach achieves significant improvement in accuracy, compared to word-level features used in many social media classification tasks. Further, we conduct experiments to study the run-time performance of such classifiers for integration with a variety of applications. Finally, a prototype implementation using an Android-based user device demonstrates how user context (intention) derived from social media sites can be consumed by novel social networking applications.
CITATION STYLE
Banerjee, N., Chakraborty, D., Joshi, A., Mittal, S., Rai, A., & Ravindran, B. (2014). Detection of real-time intentions from micro-blogs. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 131, pp. 116–128). Springer Verlag. https://doi.org/10.1007/978-3-319-11569-6_10
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