通过特征提取技术增强基于机器学习的情绪分析
Noura A Semary1, Wesam Ahmed1,2, Khalid Amin1
1Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom, Egypt.
PloS one
|February 14, 2024
概括
术语频率-反向文档频率 (TF-IDF) 是情绪分析的最佳特征提取方法,在亚马逊评论中达到99%的准确性,在Twitter数据中达到96%. 这项研究指导了未来的机器学习和特征提取研究.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 特征提取对于情感分类性能至关重要.
- 选择最佳的特征提取方法可以增强情绪分析任务.
- 机器学习和特征提取研究需要有方法的分析.
研究的目的:
- 从机器学习的角度分析和总结特征提取技术.
- 引导选择适合的特征提取方法用于情绪分析.
- 为未来的机器学习和特征提取研究提供方向.
主要方法:
- 评估的词袋 (BOW),Word2Vector,N-gram,术语频率-反向文档频率 (TF-IDF),哈希向量化器 (HV) 和词表示的全球向量 (GloVe).
- 应用特征提取技术到Twitter美国航空公司和亚马逊乐器审查数据集.
- 训练了一个随机森林分类器,使用70%的培训和30%的测试数据进行绩效评估.
主要成果:
- 术语频率-反向文档频率 (TF-IDF) 在亚马逊审查数据集上实现了99%的准确性.
- 在Twitter美国航空公司数据集上,TF-IDF实现了96%的准确性.
- 对比分析表明,TF-IDF的性能优于其他方法.
结论:
- 特征提取显著影响情绪分析模型的性能.
- TF-IDF是一种高效的特征提取技术,用于情绪分析.
- 该研究提供了改善情绪分析模型和未来研究的实用见解.
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