自我注意力双向长期短期记忆辅助自然语言处理在社交媒体平台上对刺言论的检测和分类
Jihen Majdoubi1, Taghreed Ali Alsudais2, Abeer S Almogren3
1Engineering and Technology Unit, Applied College, Majmaah University, Al Majmaah, 11952, Saudi Arabia.
Scientific reports
|December 4, 2025
概括
研究人员使用社交媒体平台上的NLP (SDCNLP-SM) 技术开发了刺的分类和检测. 这种方法在识别刺言论方面获得了94.45%的准确性,超过了现有的模型.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 计算语言学 计算语言学
- 人工智能 (AI) 是一种人工智能.
背景情况:
- 刺,一种表达负面意见的刺形式,在文本分析中提出了重大挑战,因为它依赖隐含的含义.
- 刺在社交媒体上的流行需要有效的自动检测方法来改善人机交互和信息处理.
- 刺言论的检测是自然语言处理 (NLP) 中的一个关键任务,推动了对先进分析技术的研究.
研究的目的:
- 提出和评估一种新的技术,即使用社交媒体平台上的NLP进行刺分类和检测 (SDCNLP-SM),用于自动识别刺文本.
- 提高刺言论检测模型的准确性和效率,特别是在社交媒体数据的背景下.
- 通过解决刺识别的复杂性,为正在进行的NLP研究做出贡献.
主要方法:
- 该研究采用了使用社交媒体平台上的NLP (SDCNLP-SM) 技术进行刺分类和检测,涉及数据预处理和Word2Vec文字嵌入.
- 一个双向长期短期记忆 (SA-BLSTM) 模型的自我注意力被用于核心刺的分类任务.
- 该方法结合了既有NLP方法,包括深度学习 (DL) 和机器学习 (ML) 模型,以及变压器架构.
主要成果:
- 拟议的SDCNLP-SM技术在标题数据集上的刺分类中显示出高准确率94.45%.
- 对比分析表明,SDCNLP-SM模型的性能明显优于现有的刺言论检测模型.
- Word2Vec嵌入式和SA-BLSTM架构的集成证明了对细微刺识别的有效性.
结论:
- SDCNLP-SM技术为自动刺言论检测提供了强大而准确的解决方案,特别是在社交媒体平台上.
- 这些发现强调了将SA-BLSTM等先进的NLP技术与词嵌入方法相结合的有效性,以应对复杂的语言挑战.
- 进一步的研究可以在这个模型的基础上进行,以改善在各种在线环境中对刺内容的理解和处理.
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