ABSTRACT

To find disaster relevant social media messages, current approaches utilize natural language processing methods or machine learning algorithms relying on text only, which have not been perfected due to the variability and uncertainty in the language used on social media and ignoring the geographic context of the messages when posted. Meanwhile, a disaster relevant social media message is highly sensitive to its posting location and time. However, limited studies exist to explore what spatial features and the extent of how temporal, and especially spatial features can aid text classification. This paper proposes a geographic context-aware text mining method to incorporate spatial and temporal information derived from social media and authoritative datasets, along with the text information, for classifying disaster relevant social media posts. This work designed and demonstrated how diverse types of spatial and temporal features can be derived from spatial data, and then used to enhance text mining. The deep learning-based method and commonly used machine learning algorithms, assessed the accuracy of the enhanced text-mining method. The performance results of different classification models generated by various combinations of textual, spatial, and temporal features indicate that additional spatial and temporal features help improve the overall accuracy of the classification.

摘要

为了找到与灾害相关的社交媒体消息，当前的方法利用自然语言处理方法或仅依赖文本的机器学习算法，由于社交媒体上使用的语言的可变性和不确定性以及忽略消息的地理背景，这些方法尚未完善发布时。同时，与灾难相关的社交媒体消息对其发布位置和时间高度敏感。然而，探索哪些空间特征和时间范围，尤其是空间特征可以帮助文本分类的研究有限。本文提出了一种地理上下文感知文本挖掘方法，将来自社交媒体和权威数据集的空间和时间信息与文本信息结合起来，用于对灾害相关的社交媒体帖子进行分类。这项工作设计并展示了如何从空间数据中导出不同类型的空间和时间特征，然后用于增强文本挖掘。基于深度学习的方法和常用的机器学习算法，评估了增强文本挖掘方法的准确性。由文本、空间和时间特征的各种组合生成的不同分类模型的性能结果表明，额外的空间和时间特征有助于提高分类的整体准确性。