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Geographic context-aware text mining:enhance social media message classification for situational awareness by integrating spatial and temporal features

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International Journal of Digital Earth 2021年第11期14卷 1721-1743页

作者： Christopher Scheele Manzhu Yu Qunying Huang spatial Computing and data Mining Lab Department of GeographyUniversity of Wisconsin-MadisonMadisonWIUSA Department of Geography Pennsylvania State UniversityUniversity ParkPAUSA

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.

关键词： spatial data science spatially enabled text mining situational awareness deep learning GeoAI spatial features

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