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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 (meta-classifier) 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.
摘要：
像Twitter这样的社交媒体平台是在灾难等事件发生时分享实时信息的主要来源之一。政治事件,等。在灾难期间检测资源推文是一项基本任务，因为推文包含不同类型的信息，例如基础设施损坏，资源,对灾难事件的看法和同情，等。人道主义组织和受害者发布了与资源需求和可用性（NAR）有关的推文。因此，在灾难期间检测NAR鸣叫需要可靠的方法。现有的作品没有很好地关注NAR推文检测，并且性能也很差。因此，本文主要研究灾难期间NAR推文的检测。现有工作通常在多个自然语言处理（NLP）任务上使用功能和适当的机器学习算法。最近,卷积神经网络(CNN)在文本分类问题中得到了广泛的应用。然而,它需要大量的手动标记数据。在灾难期间，没有如此大的标记数据可用于NAR推文。为了克服这个问题，提出了将卷积神经网络与传统的基于特征的分类器进行叠加来检测NAR鸣叫。在我们的方法中，我们提出了几个信息功能，如援助，需要,食物,数据包,地震,等。在分类器和CNN中使用。在另一个分类器(元分类器)中利用所学习的特征(具有信息特征的CNN和分类器的输出)来检测NAR鸣叫。分类器如SVM,KNN,决策树,在提出的模型中使用了朴素贝叶斯。从实验中,我们发现KNN（基分类器）和SVM（元分类器）结合CNN在所提出的模型中的使用优于其他算法。本文使用2015年和2016年尼泊尔和意大利地震数据集进行实验。实验结果证明，与基线方法相比，该模型取得了最好的精度。
