Publication Type
Journal Article
Version
publishedVersion
Publication Date
7-2024
Abstract
In the age of rapid internet expansion, social media platforms like Twitter have become crucial for sharing information, expressing emotions, and revealing intentions during crisis situations. They offer crisis responders a means to assess public sentiment, attitudes, intentions, and emotional shifts by monitoring crisis-related tweets. To enhance sentiment and emotion classification, we adopt a transformer-based multi-task learning (MTL) approach with attention mechanism, enabling simultaneous handling of both tasks, and capitalizing on task interdependencies. Incorporating attention mechanism allows the model to concentrate on important words that strongly convey sentiment and emotion. We compare three baseline models, and our findings show that BERTweet outperforms the standard BERT model and exhibits similar performance to RoBERTa in crisis tweets. Furthermore, we employ natural language processing techniques to extract key subject entities (e.g., police, victims) and leverage the publicly available commonsense knowledge model, COMET-ATOMIC 2020, to identify their intentions in given crisis scenarios. Evaluation of COMET-ATOMIC 2020 on subject-based intent prediction in crisis tweets reveals that BART was superior to GPT2-XL model, providing crisis responders with vital information for better decision making. Notably, the integration of sentiment and emotion classification, identification of attention words and subject-based intent prediction represents a novel methodology, not previously applied in the context of crisis scenarios.
Keywords
Crisis tweets, Emotion analysis, Intent prediction, Multi-task learning, Natural language processing, Sentiment analysis
Discipline
Databases and Information Systems | Social Media
Research Areas
Data Science and Engineering
Publication
Information Processing and Management
Volume
61
Issue
4
First Page
1
Last Page
20
ISSN
0306-4573
Identifier
10.1016/j.ipm.2024.103695
Publisher
Elsevier
Citation
WIN MYINT, Phyo Yi; LO, Siaw Ling; and ZHANG, Yuhao.
Unveiling the dynamics of crisis events: Sentiment and emotion analysis via multi-task learning with attention mechanism and subject-based intent prediction. (2024). Information Processing and Management. 61, (4), 1-20.
Available at: https://ink.library.smu.edu.sg/sis_research/8696
Copyright Owner and License
Authors-CC-BY-NC
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Additional URL
https://doi.org/10.1016/j.ipm.2024.103695