通过使用TF-IDF和ML分类器进行引用情绪分析来理解文章的两极性
Musarat Karim1, Malik Muhammad Saad Missen1, Muhammad Umer1
1Department of Computer Science & Information Technology, Islamia University, Bahawalpur, Bahawalpur, Pakistan.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了研究论文引用的情感分析,这是一种评估科学影响的新方法,超出了引用数量. 使用机器学习和TF-IDF,它准确地确定引用极性,以获得更好的文章排名.
科学领域:
- 图书统计学 图书统计学
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 情绪分析得到了广泛的研究,但在科学文章中分析引用情绪仍然是一个尚未探索的领域.
- 目前的引用指标,如引用数,并不完全代表文章的质量,因为引用可以显示弱点.
- 确定引用两极性对于准确量化文章的影响和排名至关重要.
研究的目的:
- 开发和评估一种方法来确定研究文章中引用的情感两极性.
- 探索情感分析在文献识别中的应用,以提高对科学知识的理解.
- 通过引用情绪分析,提高科学文章质量和影响的评估.
主要方法:
- 用于特征提取的频率-反向文档频率 (TF-IDF) 术语,包括单图和双图表示.
- 采用机器学习分类器,特别是额外树分类器,用于情绪极性确定.
- 使用合成少数人过量抽样技术 (SMOTE) 调查不平衡数据集的影响.
主要成果:
- 提出的方法在确定引用情绪极性方面达到99.0%的高精度.
- 额外树分类器在SMOTE过量采样数据集上训练时表现出优异的性能,具有双克TF-IDF特征.
- 实验突出了SMOTE在处理不平衡数据集的有效性,用于引用情绪分析.
结论:
- 引用的情感分析为文献计量分析和科学影响评估提供了一个有希望的新途径.
- 开发的方法提供了一种可靠的方法来量化科学文章的质量和排名.
- 机器学习分类器,特别是具有TF-IDF特征的额外树分类器和SMOTE,对于引用情绪分析是有效的.
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