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基于变压器的集体模型用于方言阿拉伯语情感分类的分类.

Omar Mansour1, Eman Aboelela1, Remon Talaat1

  • 1Intella, Cairo, Egypt.

PeerJ. Computer science
|March 28, 2025
PubMed
概括
此摘要是机器生成的。

这项研究增强了社交媒体上方言阿拉伯语的情绪分析. 一个基于变压器的组合模型实现了卓越的性能,超过了其他机器和深度学习方法.

关键词:
阿拉伯特·阿拉伯特是一个阿拉伯人.阿拉伯情绪分类 阿拉伯情绪分类阿拉伯语 阿拉伯语卡梅尔伯特 卡梅尔伯特是什么意思深度学习是一种深度学习.组合学习学习 组合学习快速文本 快速文本马尔伯特·马尔伯特:一个人.机器学习 机器学习在XLM-ROBERTA中使用.

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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
  • 社交媒体分析 社交媒体分析

背景情况:

  • 社交媒体对公众论至关重要,尤其是在全球紧急情况下.
  • 在方言阿拉伯语中分析公众情绪是具有挑战性的,因为语言复杂性.
  • 情绪分析或论挖掘是理解社会趋势的关键.

研究的目的:

  • 为了评估各种模型对方言阿拉伯语推特的情感分析.
  • 为了比较机器学习,深度学习和基于变压器的方法.
  • 确定阿拉伯情绪分类中最有效的模型.

主要方法:

  • 使用机器学习 (SVM,NB,DT,XGBoost),深度学习 (CNN,BLSTM) 和变压器模型 (CAMeLBERT,XLM-RoBERTa,MARBERT) 的实验.
  • 使用AraVec,FastText,AraBERT和TF-IDF进行特征提取.
  • 对三个基准阿拉伯语推特数据集的分析:ASTD,ASAD和TEAD.

主要成果:

  • 基于变压器的模型,特别是合体方法,表现出卓越的性能.
  • 拟议的基于变压器的组合模型实现了高精度 (90.4%),回忆 (88%),精度 (87.3%) 和F1得分 (87.7%).
  • 这表明了先进的变压器架构对方言阿拉伯情绪分析的有效性.

结论:

  • 基于变压器的组合模型对于方言阿拉伯语情绪分析非常有效.
  • 该研究为该领域的未来研究提供了强大的基准.
  • 改善阿拉伯社交媒体的情绪分析可以在危机期间带来更好的决策.