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首页> 外文会议>2017 IEEE Conference on Big Data and Analytics >Sentiflood: Process model for flood disaster sentiment analysis
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Sentiflood: Process model for flood disaster sentiment analysis

机译：Sentiflood：洪水灾难情绪分析的过程模型

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摘要

The growing utilization of Web 2.0 leads us to extract, transform, load, and analyze enormously and sizably voluminous amount of structured and unstructured data, at a speedy pace, mentioned to as `Big Data'. With the help of collected public opinion from social media, users are transforming themselves into a social sensor. Data produced by social media is believed can be important in understanding the public's reactions and feelings. Particularly for disaster management, finding posts that indicate a situation of dissatisfaction, danger or worrying may prove critical. Consequently, a systematic classification is genuinely helpful in processing these posts and classify them into sentiment polarity and aspect-based classification that will benefit diverse agencies such as non-government or government in managing such crisis situations. However, there is less work of other researchers in developing big data application using a systematic method such as methodology. Distinctly, in disaster management system that exploits sentiment analysis. Based on ATHENA project, this work extends the Crisis Information Processing Centre component by using supervised learning technique of machine learning approach with the incorporation of RUP/SOMA methodology.
机译：Web 2.0的日益普及使我们能够快速提取，转换，加载和分析大量的结构化和非结构化数据，被称为“大数据”。借助从社交媒体收集的舆论，用户正在将自己转变为社交传感器。人们认为，社交媒体产生的数据对于理解公众的反应和感受可能非常重要。特别是对于灾难管理，找到表明不满意，危险或担忧状况的职位可能至关重要。因此，系统地分类确实有助于处理这些职位，并将其分类为情感极性和基于方面的分类，这将使非政府或政府等各种机构在处理此类危机情况时受益。但是，其他研究人员在使用系统化方法（例如方法论）开发大数据应用程序方面的工作较少。显然，在利用情绪分析的灾难管理系统中。在ATHENA项目的基础上，这项工作通过将机器学习方法的监督学习技术与RUP / SOMA方法相结合，扩展了危机信息处理中心组件。

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