Conceptual Representation for Crisis-Related Tweet Classification

Authors

  • Won-Gyu Choi Jeonbuk National University, Division of Computer Science and Engineering, RCAIT
  • Kyung-Soon Lee Jeonbuk National University, Division of Computer Science and Engineering, RCAIT

DOI:

https://doi.org/10.13053/cys-23-4-3304

Keywords:

Conceptual representation, crisis-related tweets classification, incident streams, convolutional neural networks, class activation mapping, one-shot learning, support vector machines

Abstract

The importance of social media such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly recognized. During crisis situations, rapid and effective response actions by emergency services are critical to assure the safety of the public. In this paper, we propose a conceptual representation for crisis-related tweet classification. In order to classify a stream of tweets related to the incident, the crisis-related terms in each tweet are represented as conceptual entities such as event entities, category indicator entities, information type entities, URL entities, and user entities. For tweet classification, we have compared support vector machines and deep learning model which combines class activation mapping with one-shot learning in convolutional neural networks. Experimental results on TREC 2018 Incident Streams test collection show significant improvement over the baseline system.

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Published

2019-12-20

Issue

Vol. 23 No. 4 (2019): 23-4(2019)

Section

Articles

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