Chen, Qi, Wang, Wei, Huang, Kaizhu and Coenen, Frans 
ORCID: 0000-0003-1026-6649
(2022)
Zero-Shot Text Classification via Knowledge Graph Embedding for Social Media Data.
IEEE INTERNET OF THINGS JOURNAL, 9 (12).
pp. 9205-9213.
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Abstract
The idea of 'citizen sensing' and 'human as sensors' is crucial for social Internet of Things, an integral part of cyber-physical-social systems (CPSSs). Social media data, which can be easily collected from the social world, has become a valuable resource for research in many different disciplines, e.g., crisis/disaster assessment, social event detection, or the recent COVID-19 analysis. Useful information, or knowledge derived from social data, could better serve the public if it could be processed and analyzed in more efficient and reliable ways. Advances in deep neural networks have significantly improved the performance of many social media analysis tasks. However, deep learning models typically require a large amount of labeled data for model training, while most CPSS data is not labeled, making it impractical to build effective learning models using traditional approaches. In addition, the current state-of-the-art, pretrained natural language processing (NLP) models do not make use of existing knowledge graphs, thus often leading to unsatisfactory performance in real-world applications. To address the issues, we propose a new zero-shot learning method which makes effective use of existing knowledge graphs for the classification of very large amounts of social text data. Experiments were performed on a large, real-world tweet data set related to COVID-19, the evaluation results show that the proposed method significantly outperforms six baseline models implemented with state-of-the-art deep learning models for NLP.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Social networking (online), Data models, Task analysis, Sensors, Internet of Things, Bit error rate, Predictive models, Internet of Things (IoT), knowledge graph, natural language processing (NLP), social media data analysis, zero-shot learning |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 29 Jun 2021 14:51 |
| Last Modified: | 14 Mar 2024 21:44 |
| DOI: | 10.1109/JIOT.2021.3093065 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3128139 |
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