TL;DR
This study uses aspect-based sentiment analysis on Twitter data to examine how China's image changed during COVID-19, revealing a shift from positive to negative sentiments and differing attitudes among various user groups.
Contribution
First to analyze country image during COVID-19 with fine-grained aspect-based sentiment analysis on large-scale social media data.
Findings
Overall sentiment shifted from non-negative to negative.
Negative ideology-related aspects increased in mentions.
Different Twitter user groups showed varied attitudes.
Abstract
Country image has a profound influence on international relations and economic development. In the worldwide outbreak of COVID-19, countries and their people display different reactions, resulting in diverse perceived images among foreign public. Therefore, in this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset. To our knowledge, this is the first study to explore country image in such a fine-grained way. To perform the analysis, we first build a manually-labeled Twitter dataset with aspect-level sentiment annotations. Afterward, we conduct the aspect-based sentiment analysis with BERT to explore the image of China. We discover an overall sentiment change from non-negative to negative in the general public, and explain it with the increasing mentions of negative ideology-related aspects…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsLinear Layer · Softmax · Layer Normalization · Weight Decay · Dropout · Linear Warmup With Linear Decay · Dense Connections · Attention Dropout · WordPiece · Multi-Head Attention
