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ArSa-Tweets:基于深度学习模型的新型阿拉伯刺言论检测系统

Qusai Abuein1, Ra'ed M Al-Khatib2, Aya Migdady1

  • 1Department of Computer Information Systems, Jordan University of Science and Technology, Irbid, 22110, Jordan.

Heliyon
|September 16, 2024
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概括

本研究介绍了ArSa-Tweet模型,用于检测阿拉伯语推文中的刺言论,克服阿拉伯语情绪分析 (SA) 的挑战. 该模型利用先进的深度学习,实现了高精度,AraBert-V02表现最好.

关键词:
深度学习 (DL) 是指深度学习.机器学习是机器学习.自然语言处理 (NLP)刺语 刺语是一种刺语.情绪分析 (SA) 是一种情绪分析.推文 推文 推文

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科学领域:

  • 自然语言处理自然语言处理.
  • 计算语言学 计算语言学
  • 人工智能的人工智能

背景情况:

  • 在情感分析 (SA) 中检测刺言论至关重要,因为刺言论偏离了字面意思.
  • 阿拉伯语SA面临的挑战包括隐含的成语和缺乏专门的刺 corpora.
  • 现有的方法与阿拉伯刺表达的细微差别作斗争.

研究的目的:

  • 提出和开发一个新的模型,ArSa-Tweet,用于检测阿拉伯语推文中的刺言论.
  • 通过结合强大的预处理和先进的深度学习来解决现有的阿拉伯SA方法的局限性.
  • 创建一个有价值的阿拉伯刺语库,ArSa-data,用于研究和开发.

主要方法:

  • 实施和调整各种深度学习 (DL) 模型:LSTM,多头CNN-LSTM-GRU,BERT,AraBert-V01和AraBert-V02.
  • 在DL模型输入之前,应用严格的预处理技术来提高数据质量.
  • 开发ArSa-data,一个专门的阿拉伯语tweet集,用于刺分析.

主要成果:

  • ArSa-Tweet模型显示了刺言论检测准确度的显著改善.
  • 对比分析证实了AraBert-V02模型在ArSa-Tweet框架内的优越性能.
  • 提出的方法在所有评估的指标中实现了最高的准确率.

结论:

  • ArSa-Tweet模型,特别是与AraBert-V02集成,为阿拉伯刺探测提供了一个非常有效的解决方案.
  • ArSa数据库为推进阿拉伯情绪分析研究提供了宝贵的资源.
  • 这项工作有助于克服了解阿拉伯社交媒体中细微语言的关键挑战.