Mobile and Ubiquitous Systems: Computing, Networking, and Services. 10th International Conference, MOBIQUITOUS 2013, Tokyo, Japan, December 2-4, 2013, Revised Selected Papers

Research Article

Detection of Real-Time Intentions from Micro-blogs

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  • @INPROCEEDINGS{10.1007/978-3-319-11569-6_10,
        author={Nilanjan Banerjee and Dipanjan Chakraborty and Anupam Joshi and Sumit Mittal and Angshu Rai and B. Ravindran},
        title={Detection of Real-Time Intentions from Micro-blogs},
        proceedings={Mobile and Ubiquitous Systems: Computing, Networking, and Services. 10th International Conference, MOBIQUITOUS 2013, Tokyo, Japan, December 2-4, 2013,  Revised Selected Papers},
        proceedings_a={MOBIQUITOUS},
        year={2014},
        month={12},
        keywords={Social networks Micro--blogs Intention mining},
        doi={10.1007/978-3-319-11569-6_10}
    }
    
  • Nilanjan Banerjee
    Dipanjan Chakraborty
    Anupam Joshi
    Sumit Mittal
    Angshu Rai
    B. Ravindran
    Year: 2014
    Detection of Real-Time Intentions from Micro-blogs
    MOBIQUITOUS
    Springer
    DOI: 10.1007/978-3-319-11569-6_10
Nilanjan Banerjee1,*, Dipanjan Chakraborty1,*, Anupam Joshi1,*, Sumit Mittal1,*, Angshu Rai1,*, B. Ravindran2,*
  • 1: IBM Research - India
  • 2: Indian Institute of Technology
*Contact email: nilanjba@in.ibm.com, cdipanjan@in.ibm.com, anupam.joshi@in.ibm.com, sumittal@in.ibm.com, angshurai1@gmail.com, ravi@cse.iitm.ac.in

Abstract

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 ( 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.