Wang, C.L. and Zhang, J.X. (2014) Ap-plication of Improved K-Means Algorithm Based on LDA in Text Clustering. Journal of Computer Applications, 34, 249-254.

has been cited by the following article:

• TITLE: Research on Political Trend of North Korea Based on Big Data Text Mining Method

  AUTHORS: Hongyi Li, Zhezhi Jin

  KEYWORDS: Text Mining, K-Means Algorithm, LDA Topic Model, Public Opinion Analysis

  JOURNAL NAME: Open Access Library Journal, Vol.6 No.11, November 27, 2019

  ABSTRACT: With the method of text mining, this paper takes the related data of Rodong Sinmun in the recent ten years as the research object, extracts the hot topics and carries on the trend analysis. With the special attribute of his speech media, this paper analyzes the political issue. By extracting nearly one million news text data, their topic content is analyzed, combining with LDA topic model, and using K-means clustering algorithm. Aiming at the limitations of the traditional K-means algorithm, it is solved on the pre-built big data analysis platform, and the structure and content of the theme are analyzed in detail. In the end, the political theme and the trend of public opinion in recent years are derived. In terms of application, it is of great significance to analyze and study Korean big data text.