Geographic context-aware text mining: enhance social media message classification for situational awareness by integrating spatial and temporal features
- 20 August 2021
- journal article
- research article
- Published by Taylor & Francis Ltd in International Journal of Digital Earth
- Vol. 14 (11), 1721-1743
- https://doi.org/10.1080/17538947.2021.1968048
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.Keywords
Funding Information
- University of Wisconsin-Madison
- National Science Foundation
This publication has 44 references indexed in Scilit:
- Automated geographic context analysis for volunteered informationApplied Geography, 2013
- #Earthquake: Twitter as a Distributed Sensor SystemTransactions in GIS, 2012
- Assuring the quality of volunteered geographic informationSpatial Statistics, 2012
- Evaluation of Weather Radar with Pulse Compression: Performance of a Fuzzy Logic Tornado Detection AlgorithmJournal of Atmospheric and Oceanic Technology, 2011
- Twitter for crisis communication: lessons learned from Japan's tsunami disasterInternational Journal of Web Based Communities, 2011
- Crowdsourcing geographic information for disaster response: a research frontierInternational Journal of Digital Earth, 2010
- Data Mining Storm Attributes from Spatial GridsJournal of Atmospheric and Oceanic Technology, 2009
- Improving Accuracy in Word Class Tagging through the Combination of Machine Learning SystemsComputational Linguistics, 2001
- Text categorization with Support Vector Machines: Learning with many relevant featuresLecture Notes in Computer Science, 1998
- A Computer Movie Simulating Urban Growth in the Detroit RegionEconomic Geography, 1970