A stacked convolutional neural network for detecting the resource tweets during a disaster
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A stacked convolutional neural network for detecting the resource tweets during a disaster Sreenivasulu Madichetty1 · Sridevi M.1 Received: 12 October 2019 / Revised: 7 August 2020 / Accepted: 15 September 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Social media platform like Twitter is one of the primary sources for sharing real-time information at the time of events such as disasters, political events, etc. Detecting the resource tweets during a disaster is an essential task because tweets contain different types of information such as infrastructure damage, resources, opinions and sympathies of disaster events, etc. Tweets are posted related to Need and Availability of Resources (NAR) by humanitarian organizations and victims. Hence, reliable methodologies are required for detecting the NAR tweets during a disaster. The existing works don’t focus well on NAR tweets detection and also had poor performance. Hence, this paper focus on detection of NAR tweets during a disaster. Existing works often use features and appropriate machine learning algorithms on several Natural Language Processing (NLP) tasks. Recently, there is a wide use of Convolutional Neural Networks (CNN) in text classification problems. However, it requires a large amount of manual labeled data. There is no such large labeled data is available for NAR tweets during a disaster. To overcome this problem, stacking of Convolutional Neural Networks with traditional feature based classifiers is proposed for detecting the NAR tweets. In our approach, we propose several informative features such as aid, need, food, packets, earthquake, etc. are used in the classifier and CNN. The learned features (output of CNN and classifier with informative features) are utilized in another classifier (metaclassifier) for detection of NAR tweets. The classifiers such as SVM, KNN, Decision tree, and Naive Bayes are used in the proposed model. From the experiments, we found that the usage of KNN (base classifier) and SVM (meta classifier) with the combination of CNN in the proposed model outperform the other algorithms. This paper uses 2015 and 2016 Nepal and Italy earthquake datasets for experimentation. The experimental results proved that the proposed model achieves the best accuracy compared to baseline methods. Keywords NAR · CNN · Disaster · Stacking Sreenivasulu Madichetty
[email protected] Sridevi M. [email protected] 1
Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India
Multimedia Tools and Applications
1 Introduction Micro-blogging [10, 14, 36, 40] sites like Twitter, Facebook, Instagram, etc. are helpful for collecting situational information [13] during a disaster like an earthquake, floods, disease outbreaks [25], etc. During these events, minor tweets are posted relevant to the specific classes such as infrastructure damage, resources [6, 33], service requests [24], etc., and also spam tweets, communal tweets and emotion information are posted [8, 16, 17, 1
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