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An Arabic social media based framework for incidents and events monitoring in smart cities
Journal of Cleaner Production ( IF 11.1 ) Pub Date : 2019-02-14 , DOI: 10.1016/j.jclepro.2019.02.063
Manar Alkhatib , May El Barachi , Khaled Shaalan

Smart city initiatives aim at leveraging human, collective, and technological capital to ensure sustainable development and quality of life for their citizens. Offering efficient and sustainable emergency rescue services in smart cities requires coordinated efforts and shared information between the public, the decision makers, and rescue teams. With the rapid growth and proliferation of social media platforms, there is a vast amount of user-generated content that can be used as source of information about cities. In this work, we propose a novel framework for events and incidents’ management in smart cities. Our framework uses text mining, text classification, named entity recognition, and stemming techniques to extract the intelligence needed from Arabic social media feeds, for effective incident and emergency management in smart cities. In our system, the data is automatically collected from social media feeds then processed to generate incident intelligence reports that can provide emergency situational awareness and early warning signs to rescue teams. The proposed framework was implemented and tested using datasets collected from Arabic Twitter feeds over a two-years span, and the obtained results show that Polynomial Networks and Support Vector Machines are the top performers in terms of Arabic text classification, achieving classification accuracy of 96.49% and 94.58% respectively, when used with stemming. The results also showed that the use of stemming led to a penalty in terms of response time, and that the richer the dataset/corpus used in terms of size and composition, the higher the classification accuracy will be.



中文翻译:

基于阿拉伯社交媒体的框架,用于智慧城市中的事件和事件监控

智慧城市计划旨在利用人力,集体和技术资本来确保其公民的可持续发展和生活质量。在智慧城市中提供高效,可持续的紧急救援服务需要公众,决策者和救援团队之间的协调努力和共享信息。随着社交媒体平台的快速增长和扩散,大量用户生成的内容可用作有关城市的信息源。在这项工作中,我们为智慧城市中的事件和事件管理提出了一个新颖的框架。我们的框架使用文本挖掘,文本分类,命名实体识别和词干提取技术从阿拉伯文社交媒体提要中提取所需的情报,以在智慧城市中进行有效的事件和应急管理。在我们的系统中,从社交媒体提要中自动收集数据,然后对其进行处理以生成事件情报报告,这些报告可以为救援队提供紧急情况意识和预警信号。所提出的框架是使用从阿拉伯语Twitter Twitter提要中收集的数据集在两年的时间里实施和测试的,所获得的结果表明,多项式网络和支持向量机在阿拉伯语文本分类方面表现最佳,分类精度达到96.49%与茎一起使用时分别为94.58%和94.58%。结果还表明,使用词干会导致响应时间方面的损失,并且就大小和组成而言,所使用的数据集/语料库越丰富,分类精度就越高。数据是从社交媒体源中自动收集的,然后进行处理以生成事件情报报告,该报告可以为救援队提供紧急情况意识和预警信号。所提出的框架是使用从阿拉伯语Twitter Twitter提要中收集的数据集在两年的时间内实施和测试的,所获得的结果表明,在阿拉伯文本分类方面,多项式网络和支持向量机的性能最高,分类精度达到96.49%与茎一起使用时分别为94.58%和94.58%。结果还表明,使用词干会导致响应时间方面的损失,并且就大小和组成而言,所使用的数据集/语料库越丰富,分类精度就越高。数据是从社交媒体源中自动收集的,然后进行处理以生成事件情报报告,该报告可以为救援队提供紧急情况意识和预警信号。所提出的框架是使用从阿拉伯语Twitter Twitter提要中收集的数据集在两年的时间里实施和测试的,所获得的结果表明,多项式网络和支持向量机在阿拉伯语文本分类方面表现最佳,分类精度达到96.49%与茎一起使用时分别为94.58%和94.58%。结果还表明,使用词干会导致响应时间方面的损失,并且就大小和组成而言,所使用的数据集/语料库越丰富,分类精度就越高。

更新日期:2019-02-14
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