通过评估在短文本中发现的主题,通过在不同领域的数据中研究主题建模技术
R Muthusami1, N Mani Kandan2, K Saritha3
1Department of Computer Applications, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, India.
Scientific reports
|May 25, 2024
概括
本研究引入了一种新的方法,用于使用主题建模来评估短文中主题质量. 该方法通过评估主题分离和连贯性来增强在线内容的分析,以获得更好的见解.
科学领域:
- 计算语言学 计算语言学
- 数据科学数据科学数据科学
- 信息检索 信息检索
背景情况:
- 在线内容的激增需要有效的方法来分析短文本.
- 了解这些文本的主题和质量对于各种应用至关重要.
- 现有的主题建模技术在评估主题质量方面面临挑战,例如分离和连贯性.
研究的目的:
- 使用已建立的主题建模技术分析短文.
- 引入和评估一种用于评估主题质量的新方法.
- 为了比较概率和非概率主题模型的性能.
主要方法:
- 使用隐藏的迪里克莱特分配 (LDA) 进行概率主题建模.
- 用于非概率主题建模的非负矩阵因子化 (NMF).
- 应用聚类方法和轮分析,用于主题质量评估.
主要成果:
- 提出的主题评估方法表现出卓越的性能.
- 评估框架有效地评估了主题的分离和一致性.
- 使用新型评估方法分析了LDA和NMF模型.
结论:
- 新课题评价方法对于分析短文是有效的.
- 该方法提供了一种可靠的方式来评估发现的主题的质量.
- 这项研究有助于改善在线内容的语言解释.
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