Classification of Short-Texts Generated During Disasters: A Deep Neural Network Based Approach

Kundu, Shamik and Srijith, P K and Desarkar, Maunendra Sankar (2018) Classification of Short-Texts Generated During Disasters: A Deep Neural Network Based Approach. In: 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM, 28-31 August 2018, Barcelona, Spain.

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Micro-blogging sites provide a wealth of resources during disaster events in the form of short texts. Correct classification of these text data into various actionable classes can be of great help in shaping the means to rescue people in disaster-affected places. The process of classification of these text data poses a challenging problem because the texts are usually short and very noisy and finding good features that can distinguish these texts into different classes is time consuming, tedious and often requires a lot of domain knowledge. We propose a deep learning based model to classify tweets into different actionable classes such as resource need and availability, activities of various NGO etc. Our model requires no domain knowledge and can be used in any disaster scenario with little to no modification.

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IITH Creators:
IITH CreatorsORCiD
Desarkar, Maunendra SankarUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Indexed in Scopus
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Library Staff
Date Deposited: 28 Oct 2019 11:17
Last Modified: 28 Oct 2019 11:17
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