使用提升杀手掠夺策略对推特的情感分类进行了优化等级CLSTM模型
T Nithya1, M Siva Ramkumar2, Rajendran Thavasimuthu3
1Department of Computer Science and Engineering, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, India. nithya.t@ritchennai.edu.in.
Scientific reports
|August 29, 2025
概括
这项研究引入了最佳分层卷积神经长期短期记忆 (OTCNLSTM) 模型,用于增强社交媒体情绪分析. OTCNLSTM模型显著提高了对推特情绪的分类准确性, 超过了现有的方法.
科学领域:
- 自然语言处理
- 机器学习
- 计算语言学
背景情况:
- 社交媒体产生大量用户生成的内容,增加了论挖掘的复杂性.
- 现有的情绪分析 (SA) 系统由于数据限制和复杂的模型配置而难以预测准确性,阻碍了深度学习 (DL) 应用.
- 准确的SA对于了解产品,进步和社会问题至关重要.
研究的目的:
- 开发一个改进的情感分析模型来分类推文中的情感.
- 解决当前SA系统的局限性,特别是深度学习模型的低准确性和预测率.
- 从文本数据中提取本地情感.
主要方法:
- 提出了一个最佳分层卷积神经长期短期记忆 (OTCNLSTM) 模型,用于情感识别的分类学习.
- 在TCNLSTM模型中使用了四个训练块来进行层次的局部特征提取.
- 实施了增强杀手捕食 (BKWOP) 战略,用于超参数优化和稳定的神经网络模型构建.
- 使用Kaggle Twitter数据集进行比较实验以评估模型性能.
主要成果:
- 与其他情绪分析模型相比,OTCNLSTM模型在推特情绪分类方面表现出卓越的表现.
- 提出的模型有效地按层级提取当地情绪.
- BKWOP战略成功地确定了构建稳定的神经网络的最佳超参数.
结论:
- OTCNLSTM模型为社交媒体数据的情绪分析准确性提供了显著的进步.
- 层次特征提取方法提高了模型识别文本中细微情感的能力.
- 这项研究为实时商业情绪分析应用提供了更强大,更准确的解决方案.
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