An ALBERT-based TextCNN-Hatt hybrid model enhanced with topic knowledge for sentiment analysis of sudden-onset disasters
发布时间：2025-09-07
点击次数：
- DOI码：
- 10.1016/j.engappai.2023.106136
- 发表刊物：
- ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- 摘要：
- Sudden-onset disasters put forward new requirements for on the state authorities' ability to analyze public opinion sentiment. However, traditional sentiment analysis methods ignore the contextual semantic relation-ships , out-of-vocabulary words , their computational resource utilization is excessive compared to their expected accuracy. In this paper, an ALBERT-based model combined with a text convolution neural network, a hierarchical attention mechanism and the latent Dirichlet allocation is proposed to create a hybrid model enhanced with topic knowledge for sentiment analysis of sudden-onset disasters. Weibo text data from a rainstorm disaster in China are used to evaluate the model's performance. Compared with the XLNet, DistilBERT and RoBERTa models, the experimental results demonstrate that the proposed approach is capable of achieving better performance by incorporating external topic knowledge into the language representation model to compensate for the limited vocabulary data.
- 论文类型：
- 期刊论文
- 卷号：
- 123
- ISSN号：
- 0952-1976
- 是否译文：
- 否
- 发表时间：
- 2023-01-01
- 收录刊物：
- SCI