采用BiGRU和RNN基于LSTM深度学习模型的产品评论情绪分析的现代化方法
L Godlin Atlas1, Daniel Arockiam2, Arvindhan Muthusamy2
1Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, India.
Scientific reports
|May 13, 2025
概括
这项研究引入了一种用于在线产品评论的新情绪分析模型. 利用自然语言处理和深度学习,它准确地对电子商务洞察力的评论极性进行分类.
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.
- 电子商务分析 电子商务分析
背景情况:
- 网络2.0和在线购物的兴起导致用户生成的内容激增.
- 公司越来越多地使用论挖掘来分析市场需求和产品接收.
- 情绪分析对于了解电子商务行业消费者意见至关重要.
研究的目的:
- 提出一种新的句子级情绪分析方法,用于挖掘在线产品评论.
- 开发一个模型,预处理,提取特征,并对情绪极性进行分类.
- 评估模型的性能与现有技术相比.
主要方法:
- 用于产品评论收集的网络爬虫.
- 预处理的自然语言处理技术 (标记化, lemmatization,停止词删除,命名实体识别,语音标签的一部分).
- 双向门式循环单元 (BiGRU) 用于特征提取.
- 混合循环神经网络 (RNN) 与长期短期记忆 (LSTM) 进行情绪分类 (积极,消极,中立).
主要成果:
- 拟议的模型实现了有希望的性能指标 (准确性,精度,回忆,F-测量,AUC).
- 与深度卷积神经网络 (CNN),多层感知器 (MLP),囊网络 (CapsuleNet) 和生成对抗网络 (GANs) 等现有模型进行比较,显示出优异的结果.
- 这种新的技术组合和在线产品评论的应用证明了其有效性.
结论:
- 开发的情绪分析模型为分析在线产品评论提供了一种新且有效的方法.
- 该模型在市场研究,社交网络挖掘,推系统,品牌分析和产品质量管理方面具有广泛的应用.
- 这些发现表明,深度学习和NLP在增强电子商务智能方面的潜力很大.
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