使用CNN功能和集合学习模型进行科学文本引文分析
1Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
PloS one
|May 28, 2024
概括
这项研究引入了一个新的框架来分析研究文章中的引用情绪,超越了简单的重要性指标. 拟议的方法使用投票分类器和卷积神经网络准确地分类引用情绪,改善学术影响评估.
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:
- 引用对于评估学术成就至关重要,但往往被平等对待,忽视了定性细微差别.
- 目前的方法主要依赖于定量指标,忽视了引用所传达的情绪和重要性.
- 现有的定性引用分析通常使用二进制分类 (重要/不重要),限制其范围.
研究的目的:
- 开发一个新的框架,用于研究文章中文本引用的多类情绪分析.
- 为了应对引用情绪分析中数据不平衡的挑战.
- 在引用评估中与定量指标一起纳入定性方面.
主要方法:
- 使用卷积神经网络 (CNN) 提取特征.
- 使用组合后勤回归 (LR) 和随机梯度下降 (SGD) 的投票分类器进行分类.
- 处理类不平衡与合成少数群体过量采样技术 (SMOTE).
主要成果:
- 拟议的框架实现了0.99.9的完美得分 (准确性,精度,回忆,F1).
- 与使用术语频率 (TF) 和TF-反向文档频率 (TF-IDF) 的传统方法相比,表现出卓越的性能.
- 在不平衡的数据集上有效处理多类情绪分类.
结论:
- 这项研究通过结合定性评估,推进了学术引用中的情感分析.
- 拟议的框架为评估引文影响提供了一种更细致,更准确的方法.
- 强调需要整合定性情绪分析来进行全面的引用评估.
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