Geographic context-aware text mining: enhance social media message classification for situational awareness by integrating spatial and temporal features
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 c...
Main Authors: | Christopher Scheele, Manzhu Yu, Qunying Huang |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2021-11-01
|
Series: | International Journal of Digital Earth |
Subjects: | |
Online Access: | http://dx.doi.org/10.1080/17538947.2021.1968048 |
Similar Items
-
Deep learning for real-time social media text classification for situation awareness – using Hurricanes Sandy, Harvey, and Irma as case studies
by: Manzhu Yu, et al.
Published: (2019-11-01) -
Improved Graph Neural Networks for Spatial Networks Using Structure-Aware Sampling
by: Chidubem Iddianozie, et al.
Published: (2020-11-01) -
Spatial Turn and Situational Approach: Ethical Dimension
by: Інна Голубович, et al.
Published: (2023-04-01) -
Introduction to Big Data Computing for Geospatial Applications
by: Zhenlong Li, et al.
Published: (2020-08-01) -
Gender Characteristics on Gaze Movement in Situation Awareness
by: Yejin Lee, et al.
Published: (2021-11-01)