对深度假冒X帖子的情绪分析使用基于新型转移学习的词嵌入和混合LGR方法
Madiha Khalid1, Muhammad Faheem Mushtaq1, Urooj Akram1
1Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan.
Scientific reports
|August 3, 2025
概括
这项研究引入了一种新的深度学习方法,用于分析深度假文本中的情绪,在识别误导性在线内容和打击假新闻方面达到99%的准确性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 社交媒体分析 社交媒体分析
背景情况:
- 社交媒体推动内容共享,包括用于错误信息的AI生成深度假文.
- 现有的情绪分析与深度假冒内容的细微差别作斗争,未能捕捉到其误导性质.
- 准确地了解公众对深度假冒帖子的看法越来越具有挑战性.
研究的目的:
- 提出一种混合深度学习 (DL) 和转移学习 (TL) 方法,用于深度假冒帖子的情绪分析.
- 开发新的基于TL的功能,将LSTM和DT结合起来,以增强语境理解.
- 根据各种机器学习 (ML) 和DL技术对拟议的方法进行评估.
主要方法:
- 开发了一种混合DL模型,包括LSTM,GRU和RNN (LGR).
- 新型转移学习 (TL) 功能是通过结合LSTM和决策树 (DT) 来设计的.
- 模型使用多种特征提取技术 (BOW,TF-IDF,词嵌入) 进行训练,并通过k-fold交叉验证进行验证.
主要成果:
- 建议使用新型TL特征的LGR方法在情绪分析中实现了99%的准确性.
- 混合DL和TL方法的性能优于现有的ML和DL技术.
- 广泛的超参数调整提高了ML模型的性能和效率.
结论:
- 开发的方法有效地检测和缓解深度假冒内容的传播.
- 这项研究提供了一种可扩展的机制,用于监测和减少在线错误信息的影响.
- 这些发现解决了分析深度假冒特定社交媒体情绪的关键差距.
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