HGATT_LR:转换复习文本分类与超图的注意层和后勤回归
S Pradeepa1, Elizabeth Jomy2, S Vimal3
1Department of Information Technology, School of Computing, SASTRA Deemed University, Thanjavur, Tamilnadu, 613401, India.
Scientific reports
|August 23, 2024
概括
本研究引入了一种新的超图注意层与后勤回归 (HGATT_LR) 进行有效的文本分类. HGATT_LR模型在亚马逊评论中达到88%的准确性,超过了复杂数据分析的现有方法.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 文本分类对于情绪分析,意见挖掘和客户反至关重要.
- 超图算法有效地捕获文本数据中的复杂关系.
- 对于复杂的现实世界数据集,现有的方法需要改进.
研究的目的:
- 为增强文本分类提出一个新的超图注意层与后勤回归 (HGATT_LR) 模型.
- 为了评估HGATT_LR在亚马逊审查数据集上的表现.
- 为了证明超图方法在处理复杂文本交互中的优越性.
主要方法:
- 文字预处理,使用隐性狄里克莱特分配 (LDA) 提取关键词,使用节点级和边缘级的特征选择.
- 开发一个与物流回归 (HGATT_LR) 集成的超图注意层.
- 与亚马逊审查数据集上的最先进的文本分类器进行比较分析.
主要成果:
- 拟议的HGATT_LR模型在文本分类中实现了88%的准确性.
- 与其他评估的文本分类算法相比,HGATT_LR表现出优越的性能.
- 该模型被证明是可扩展和适应更大的数据集.
结论:
- 超图注意网络为文本分类提供了一种强大的方法,特别是对于具有复杂相互依赖性的数据集.
- HGATT_LR模型为现实世界的文本分析提供了可扩展和有效的解决方案.
- 这项研究使企业能够通过先进的文本分析来提高产品质量.
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