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

作　　者：Christopher Scheele Manzhu Yu Qunying Huang 

机构地区：[1]Spatial Computing and Data Mining Lab,Department of Geography,University of Wisconsin-Madison,Madison,WI,USA [2]Department of Geography,Pennsylvania State University,University Park,PA,USA

出　　处：《International Journal of Digital Earth》2021年第11期1721-1743,共23页国际数字地球学报（英文）

基　　金：the funding support from the Vilas Associates Competition Award at University of Wisconsin-Madison(UW-Madison);the National Science Foundation[grant number 1940091].

摘　　要：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 

分 类 号：H31[语言文字—英语]