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An attention-based hybrid model for spatial and temporal sentiment analysis of COVID-19 related tweets in the contiguous United States

Bingnan Li Danielle Hutchinson Samsung Lim Chandini Raina MacIntyre

地球空间信息科学学报（英文版）2025，Vol.28Issue(4)：中插20,1846-1865,21.

地球空间信息科学学报（英文版）2025，Vol.28Issue(4)：中插20,1846-1865,21.DOI:10.1080/10095020.2024.2408343

An attention-based hybrid model for spatial and temporal sentiment analysis of COVID-19 related tweets in the contiguous United States

Bingnan Li ¹Danielle Hutchinson ¹Samsung Lim ²Chandini Raina MacIntyre¹

作者信息

1. Kirby Institute,Faculty of Medicine and Health,University of New South Wales,Sydney,Australia
2. Kirby Institute,Faculty of Medicine and Health,University of New South Wales,Sydney,Australia||School of Civil and Environmental Engineering,University of New South Wales,Sydney,Australia
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摘要

关键词

Sentiment analysis/BiLSTM/deep learning/attention mechanism/COVID-19

Key words

Sentiment analysis/BiLSTM/deep learning/attention mechanism/COVID-19

引用本文复制引用

Bingnan Li,Danielle Hutchinson,Samsung Lim,Chandini Raina MacIntyre..An attention-based hybrid model for spatial and temporal sentiment analysis of COVID-19 related tweets in the contiguous United States[J].地球空间信息科学学报（英文版）,2025,28(4):中插20,1846-1865,21.

基金项目

The authors disclosed receipt of the following financial support for the research,authorship,and/or publication of this article:Funding was provided by the Medical Research Future Fund Research Grant[ID RFRHPI000280],Stage 1 from the Australian Government.Bingnan Li and Samsung Lim are supported from the Balvi Filantropic Fund. （）

地球空间信息科学学报（英文版）

ISSN：1009-5020

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