基于K-means-NMF测量文献主题演化路径的识别方法

IF 0.6 4区 管理学 Q3 INFORMATION SCIENCE & LIBRARY SCIENCE
Wenbo Cui, Li Jinling, Tao Zhang, Sibo Zhang
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引用次数: 0

摘要

本文针对当前主题演化分析研究中存在的主题聚类效果不明显、聚类结果混合程度高、主题演化路径不明确等问题,提出了一种基于K-means-NMF的测量文献主题演化路径的识别方法。首先,我们将K-means聚类算法与NMF模型相结合,对传统的NMF (non - negative Matrix Factorization)主题模型进行了改进,提高了主题聚类的准确性,降低了主题之间的相关性;其次,基于聚类结果进行主题共现分析,识别出重要的主题类别,用于识别关键进化路径,解决实验中存在多种可能进化路径的问题。第三,采用Word2Vec模型计算语义上下文中的主题词向量,提高相邻阶段主题间相关强度的准确性。最后,我们采用上述方法,以智能算法为例进行实证研究。实验结果表明,该研究方法可以有效识别学科领域的重要课题和课题动态,为科学研究和科技政策制定提供支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Recognition Method of Measuring Literature Topic Evolution Paths Based on K-means-NMF
In this study, we propose a recognition method of measuring literature topic evolution paths based on K-means-NMF in order to address problems such as the unobvious effect of topic clustering, high degree of mixing in clustering results, and unclear topic evolution paths that exist in the current research of topic evolution analysis. Firstly, we enhance the traditional NMF (Nonnegative Matrix Factorization) topic model by combining the K-means clustering algorithm with the NMF model to improve the accuracy of topic clustering and reduce the correlation among topics. Secondly, we perform the topic co-occurrence analysis based on the clustering results to identify important topic categories for recognizing critical evolution paths to solve the problem of multiple possible evolution paths in the experiment. Thirdly, we adopt the Word2Vec model to calculate topic word vectors in a semantic context to improve the accuracy of the correlation strength between topics at adjacent stages. Finally, we adopt the above method to conduct an empirical study using intelligent algorithms as an example. The experimental results show that this research method effectively identifies important topics and topic developments in the subject area, which can support scientific research and science and technology policy development.
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来源期刊
Knowledge Organization
Knowledge Organization INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
1.40
自引率
28.60%
发文量
7
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