Towards Analyzing Micro-Blogs for Detection and Classification of Real-Time Intentions


  • Nilanjan Banerjee IBM Research - India
  • Dipanjan Chakraborty IBM Research - India
  • Anupam Joshi IBM Research - India
  • Sumit Mittal IBM Research - India, New Delhi
  • Angshu Rai IBM Research - India
  • Balaraman Ravindran Indian Institute of Technology, Madras



Social Networks, Micro-blogs, Intention Mining


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 net- working 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 detect- ing 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.




How to Cite

Banerjee, N., Chakraborty, D., Joshi, A., Mittal, S., Rai, A., & Ravindran, B. (2021). Towards Analyzing Micro-Blogs for Detection and Classification of Real-Time Intentions. Proceedings of the International AAAI Conference on Web and Social Media, 6(1), 391-394.