利用文本信息进行社交媒体新闻分类和情绪分析
Mahmudul Hasan1, Tanver Ahmed2, Md Rashedul Islam1
1Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh.
PloS one
|July 15, 2024
概括
这项研究引入了一种高效的基于随机梯度下降的梯分类器,用于新闻分类和情绪分析. 拟议的方法达到98.12%的准确性,在资源有限的环境中表现优于复杂模型.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 社交媒体改变了沟通,增加了对情绪分析和新闻分类的需求.
- 现有的机器学习 (ML) 模型通常需要大量的资源,这阻碍了在受限制的环境中部署.
研究的目的:
- 为新闻分类和情绪分析开发一个高效的ML模型.
- 在实际应用中解决资源密集型模型的局限性.
主要方法:
- 使用TextBlob来确定新闻头条上的情绪极性.
- 提出了一个基于随机梯度下降 (SGD) 的山脊分类器 (RC),集成了先进的字符串处理.
- 对SGDR分类器与现有的监督和无监督ML算法进行了评估.
主要成果:
- 串处理管道显著提高了所有测试的ML模型的性能.
- 集体SGDR分类器实现了98.12%的准确性,超过了最先进的算法.
- 麦克纳马的测试显示,与K-Nearest Neighbor,决策树和AdaBoost (1%级) 以及其他算法 (5%级) 相比,显著改善.
结论:
- 与基于树的和非线性方法相比,线性模型在新闻分类方面表现出卓越的熟练程度.
- 拟议的SGDR方法提供了一个可扩展和有效的解决方案,用于新闻分类和情绪分析.
- 这些发现突显了SGDR分类器在资源有限的环境中的潜力.
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