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利用食品配送服务的情绪分析,使用深度学习和词嵌入进行评论
Dheya Mustafa1, Safaa M Khabour2, Mousa Al-Kfairy3
1Department of Computer Engineering, Faculty of Engineering, The Hashemite University, Zarqa, Jordan.
PeerJ. Computer science
|March 10, 2025
概括
本研究介绍了阿拉伯食品配送服务 (FDS) 评论的高级情绪分析 (SA). 利用深度学习和NLP,它在理解客户反方面实现了高准确性,改善了在线服务评估.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 机器学习 (ML) 是指机器学习.
- 计算语言学 计算语言学
背景情况:
- 客户反对于改善在线服务至关重要,特别是食品配送服务 (FDS).
- 由于语言复杂性和资源短缺,阿拉伯语的情感分析 (SA) 研究是有限的.
- 现有的SA研究主要集中在英语上,在理解阿拉伯消费者情绪方面存在差距.
研究的目的:
- 对阿拉伯语FDS评论进行全面的情绪分析,包括现代标准阿拉伯语和方言阿拉伯语.
- 探索和评估阿拉伯SA的各种深度学习模型和NLP技术.
- 评估阿拉伯语情感分类中的词嵌入和源源方法的有效性.
主要方法:
- 使用了来自Talabat.com的手动注释的FDS数据集,包括现代标准阿拉伯语和方言阿拉伯语.
- 使用深度学习模型:卷积神经网络 (CNN),双向长期短期记忆 (BiLSTM) 和混合LSTM-CNN模型.
- 研究了不同的词嵌入和干技术,用于特征提取和预处理.
主要成果:
- 在FDS审查中,在多类分类中达到约84%,在二元分类中达到92.5%的准确性.
- 验证了酒店阿拉伯评价数据集 (HARD) 的方法,达到多类的88.9%,二元分类的97.2%.
- 证明了在不同阿拉伯语数据集和领域提出的方法的稳定性.
结论:
- 开发的情绪分析模型对于分析FDS领域的阿拉伯客户反是有效的.
- 该研究强调了深度学习和NLP在克服阿拉伯语SA.挑战方面的潜力.
- 这些发现为寻求通过反分析了解和增强客户体验的FDS公司提供了宝贵的见解.
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