ArabBert-LSTM:改进基于变压器模型和长短期记忆的阿拉伯情绪分析
Wael Alosaimi1, Hager Saleh2,3,4, Ali A Hamzah5
1Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.
Frontiers in artificial intelligence
|July 17, 2024
概括
这项研究引入了一种用于阿拉伯情绪分析的新型深度学习模型,达到97%以上的准确性. 这种方法有效地处理阿拉伯语.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 情绪分析自动化了来自文本数据的观点挖掘,跨越社交媒体等平台.
- 阿拉伯文本呈现出独特的形态复杂性,挑战了情感分析.
- 现有的方法往往难以捕捉阿拉伯语言的细微差别,以准确地分类情绪.
研究的目的:
- 提出和评估一个深度学习模型,以增强阿拉伯语情绪分析.
- 在情感分类中解决阿拉伯语言的形态复杂性.
- 为了提高阿拉伯文文本情绪分析的准确性和可靠性.
主要方法:
- 这是一个混合深度学习模型,它结合了Arabert (基于变压器的阿拉伯语言理解模型) 的词嵌入和长短期记忆 (LSTM) 的序列建模.
- 使用feedforward神经网络和输出层进行分类.
- 与传统的机器学习和其他深度学习算法进行比较,使用四个阿拉伯数据集上的各种矢量化技术 (TF-IDF,ArabBert,CBOW,skipGrams).
主要成果:
- 与基线方法相比,拟议的阿拉伯特-LSTM模型显著提高了情绪分析的准确性.
- 在阿拉伯语情绪分析任务中实现了超过97%的准确率.
- 证明了变压器模型和LSTM在捕获语境信息和阿拉伯文文本中的长期依赖性方面的有效性.
结论:
- 开发的深度学习框架为阿拉伯语情绪分析提供了强大的解决方案.
- 利用像Arabert这样的变压器模型和LSTM的序列建模对阿拉伯文本非常有效.
- 这项研究推进了阿拉伯情绪分析领域,为论挖掘提供了更准确,更可靠的工具.
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