AEGA:基于ANOVA的增强特征选择和在线客户评论分析的扩展遗传算法
Gyananjaya Tripathy1, Aakanksha Sharaff1
1Department of Computer Science and Engineering, National Institute of Technology, Raipur, Chhattisgarh 492010 India.
概括
这项研究引入了一种混合方法,使用增强的遗传算法 (GA) 和差异分析 (ANOVA) 来进行情绪分析. 该方法显著减少了特征,同时提高了在线评论中的分类准确性.
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:
- 情绪分析旨在了解在线平台的用户意见,以提高性能.
- 在线评论中的高维特征集对准确的分类构成了挑战.
- 现有的特征选择技术很难以最小的特征实现高精度.
研究的目的:
- 开发一种有效的混合方法来进行情绪分析,使用增强的遗传算法 (GA) 和差异分析 (ANOVA).
- 为了在显著减少的特征数量下实现高分类准确性.
- 为了克服分类模型中局部最小值的收问题.
主要方法:
- 开发了一种混合方法,将增强的遗传算法 (GA) 与差异分析 (ANOVA) 结合起来.
- 采用了独特的两阶段交叉和选择策略,以加强勘探和融合.
- 使用ANOVA,大大减少了功能大小,最大限度地降低了计算负担.
主要成果:
- 拟议的混合方法在亚马逊评论数据集上实现了78.60%的准确性和79.38%的F1得分.
- 在餐厅客户评价数据集中,该模型获得了77.70%的准确性和78.24%的F1评分.
- 这种新的方法超过了现有的算法,对各自的数据集使用的特征分别减少了约45%和42%.
结论:
- 混合GA-ANOVA方法对于情绪分析是有效的,提供高准确度与减少的特征集.
- 与传统分类器和优化算法相比,该方法表现出优越的性能.
- 这种方法有效地最大限度地降低了计算负载,同时在审查分析中保持了高分类性能.
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