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可扩展的深度学习框架用于情绪分析预测,用于在线电影评论

Peter Atandoh¹, Fengli Zhang¹, Mugahed A Al-Antari²

¹School of Information and Software Engineering, University of Electronic Science and Technology of China, North Jianshe Road, Chengdu, 610054, Sichuan, China.

|May 24, 2024

概括

本研究介绍了PEW-MCAB模型用于电影评论中的情感分析. 深度学习方法通过分析文字顺序和文本中隐藏的含义来实现高准确度.

科学领域:

自然语言处理自然语言处理.
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

情绪分析对于电子商务,特别是在线电影行业至关重要.
了解评论中的单词顺序和隐藏含义可以增强情绪分析.

研究的目的:

为情感分析提供增强的文本表示和深度学习模型.
分析单词信息顺序,揭示在线电影评论中隐藏的含义.

主要方法:

开发了PEW-MCAB模型,使用定位嵌入和预训练的手套嵌入矢量 (PEW) 来表示文本.
集成了多通道卷积神经网络 (MCNN) 与基于注意力的双向长期短期记忆 (AB) 模型.
在四个数据集上评估模型:IMDB,MR (2002),MRC (2004) 和MR (2005).

主要成果:

在PEW-MCAB模型实现了高准确率:90.3% (IMDB),84.1% (MR 2002),85.9% (MRC 2004),和87.1% (MR 2005).
该模型通过将全文视为一个统一的部分来有效地分类情绪.

结论:

拟议的PEW-MCAB模型在实际应用中显示出对有效和有竞争力的情绪分析的重大前景.
增强的文本表示和深度学习架构有助于提高分析在线电影评论的性能.

关键词:

注意力注意力注意力注意力双向的长期短期记忆.卷积神经网络是一种卷积神经网络.情绪分析是一种情绪分析.文本表示表示文本表示

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