一个基于注意力的新型深度学习模型,用于在2023年卡赫拉曼马拉斯/土耳其地震事件发生后改善情绪分类
Serpil Aslan1, Muhammed Yildirim2
1Software Engineering, Malatya Turgut Ozal University, Malatya, Turkey.
PeerJ. Computer science
|June 26, 2025
概括
这项研究引入了一种新的深度学习模型,用于在地震后分析推特上的公众情绪. MConv-BiLSTM-GAM模型实现了93.32%的准确性,提供更快的灾难影响洞察力.
科学领域:
- 自然语言处理自然语言处理.
- 深度学习 (Deep Learning) 是一种深度学习.
- 社交媒体分析 社交媒体分析
背景情况:
- 推特是实时公众情绪的重要来源,特别是在灾难期间.
- 传统的地震影响评估是耗时且昂贵的.
- 社交媒体上的情绪分析 (SA) 可以快速了解公众的看法.
研究的目的:
- 提出一种新的深度学习模型,MConv-BiLSTM-GAM,用于对与卡拉曼马拉斯地震有关的推文的情感分类.
- 为了利用注意力机制,在情感分析中增强语义理解.
- 为灾难应对和政策制定提供及时和客观的数据.
主要方法:
- 使用FastText进行推特矢量化.
- 开发了一个混合模型,将卷积神经网络 (CNN) 和循环神经网络 (RNN) 结合起来.
- 在双向长期短期记忆 (BiLSTM) 网络中集成了一个全球注意力机制 (GAM),用于序列学习和特征提取.
主要成果:
- MConv-BiLSTM-GAM模型的情绪分类准确率达到了93.32%.
- 拟议的模型的性能大约比现有的深度学习模型高出3%.
- 展示了基于注意力的深度学习对分析灾难相关公众情绪的有效性.
结论:
- MConv-BiLSTM-GAM模型提供了一种高度准确和高效的方法,用于对地震相关推特的实时情绪分析.
- 这种方法可以显著帮助政策制定者和决策者在灾害管理和支持方面.
- 在危机期间分析公众情绪为社会反应和情绪动态提供了宝贵的见解.
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