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集体语言学和深度学习技术的整合,以在文本数据上增强语义驱动的情感检测

Badriyya B Al-Onazi¹, Hassan Alshamrani², Hatim Dafaalla³

¹Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

Scientific reports

|November 27, 2025

概括

这项研究引入了一种基于文本的情感识别的新方法,达到99.26%的准确性. 该方法整合了语言特征和深度学习模型,以增强情绪分析.

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
机器学习机器学习

背景情况:

语料库语言学为分析语言结构和模式提供了一个框架.
基于文本的情感识别 (TER) 使用语言特征,从注释的 corpora 训练机器学习 (ML) 模型.
整合基于库和数据驱动的方法可以提高情绪识别准确性,用于诸如情绪分析和心理健康评估等应用.

研究的目的:

提出一个有效的文本情感识别和分析方法,以改善情感理解.
通过集成字向量表示和多模型深度神经网络 (EDTIWVR-MDNN) 方法开发文本中的情感检测.

主要方法:

文本预处理以提取相关数据.
使用术语频率-反向文档频率 (TF-IDF),来自变压器的双向编码器表示 (BERT) 和GloVe的融合进行文字嵌入.
使用基于注意力机制的混合时间卷积网络和双向门循环单元 (AM-T-BiG) 模型进行分类.

主要成果:

通过EDTIWVR-MDNN方法,在从文本数据集中检测情绪方面实现了99.26%的卓越准确性.
实验评估表明,在情绪识别任务中,与现有的方法相比,有显著的改进.
集成的词向量表示和多模型深度神经网络有效地捕获文本中的复杂情感表达.

结论:

拟议的EDTIWVR-MDNN方法显著提高了文字情感识别的准确性和深度.
这种方法为情绪分析,社交媒体监控,心理健康评估和人机交互提供了宝贵的改进.
先进的NLP技术和深度学习模型的融合为理解自然语言中的情绪提供了一个强大的框架.

关键词:

语文学的语料库.数据驱动的方法是数据驱动的方法.深度神经网络是一种深度神经网络.自然语言处理自然语言处理.文本式情感识别情感识别

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