通过使用基于布尔运算符的粒子群集优化来改进特征选择来进行情感分类.
Harish Dutt Sharma1, Raja Rao Budaraju2, Neeraj Kumar3
1Uttaranchal School of Computing Sciences, Uttaranchal University, Dehradun, Uttarakhand, 248007, India.
Scientific reports
|November 6, 2025
概括
本研究介绍了一种基于布尔运算符的粒子群集优化 (BOPSO) 的新方法,用于情绪分析. 博普索有效地减少了特征,提高了分类准确性,优于现有方法.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 计算智能是一种计算智能.
背景情况:
- 情绪分析对于论挖掘至关重要,但由于特征冗余性,在高维文本数据中面临挑战.
- 有效的特征选择是提高情绪分类准确性的关键.
研究的目的:
- 引入一种基于布尔运算符的粒子群集优化 (BOPSO) 算法,用于在情绪分类中增强特征选择.
- 通过解决高维数据挑战,提高情绪分析模型的效率和准确性.
主要方法:
- 通过将布尔逻辑运算符 (增子,减子,XOR) 集成到粒子集群优化 (PSO) 中来开发BOPSO,用于二进制特征选择.
- 评估了9个基准情绪数据集的BOPSO,使用了5个基于过器的客观函数 (Chi-Square,相关性,增益比,信息增益,对称不确定性).
- 使用纯贝叶斯,支持矢量机 (SVM) 和人工神经网络 (ANN) 分类器评估分类性能.
主要成果:
- 与最先进的优化技术 (DE,GWO,ABC,CS) 相比,BOPSO的平均精度提高了1.8%至4.5%.
- 在笔记本电脑数据集上实现了高达100%的准确性,显示出卓越的精度,回忆和F1分数.
- 有效地减少了特征维度,同时显著提高了情绪分类性能.
结论:
- 拟议的BOPSO算法是情绪分析中特征选择的高效方法.
- 与现有的优化技术相比,BOPSO在分类准确性和效率方面提供了显著的改进.
- 这种方法有望在各种应用领域推进情绪分析.
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