Geographic context-aware text mining: enhance social media message classification for situational awareness by integrating spatial and temporal features
Geographic context-aware text mining: enhance social media message classification for situational awareness by integrating spatial and temporal features
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DOI：
10.1080/17538947.2021.1968048
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发表时间：
2021-08
影响因子：
5.1
通讯作者：
C. Scheele;Manzhu Yu;Qunying Huang
中科院分区：
文献类型：
--
作者：
C. Scheele;Manzhu Yu;Qunying Huang
摘要为了发现与灾难相关的社交媒体消息，目前的方法使用自然语言处理方法或仅依赖于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，以及在发布消息时忽略了消息的地理上下文，这些方法尚未完善。与此同时，与灾难相关的社交媒体信息对发布位置和时间高度敏感。然而，对于什么空间特征以及时间特征，特别是空间特征对文本分类有多大的帮助，目前的研究还很有限。本文提出了一种地理上下文感知的文本挖掘方法，融合了来自社交媒体和权威数据集的时空信息，以及文本信息，用于对与灾难相关的社交媒体帖子进行分类。这项工作设计并演示了如何从空间数据中提取不同类型的空间和时间特征，然后使用这些特征来增强文本挖掘。采用基于深度学习的方法和常用的机器学习算法，对改进后的文本挖掘方法的准确率进行了评估。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，附加的空间和时间特征有助于提高分类的整体准确率。
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.