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| Theme Analysis Based on Keyword Importance and Affinity Propagation Clustering |
| Li Hailin1,2, Wan Xiaoji1, Lin Chunpei1 |
1. College of Business Administration, Huaqiao University, Quanzhou 362021;
2. Research Center of Applied Statistics and Big Data, Huaqiao University, Xiamen 361021 |
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Abstract In view of the fact that co-occurrence analysis lacks consideration of keyword importance and theme analysis in such a way that it does not adaptively extract the core themes in traditional scientific measurement methods, this paper proposes a theme analysis method based on keyword importance and affinity propagation clustering. Based on probable behavior of most authors, the method collects the keywords of theses according to the strength or weakness of the relevance to the corresponding research content, computes the importance measure of the keywords in the papers, and constructs the similarity matrix of the keywords. The extraction and analysis of the core theme is achieved through combining the method with affinity propagation clustering that can retrieve the best representative member of the cluster. In this study, the keywords in a specialized journal of literature and information during the period of 2012 to 2016 were collected, and keyword clustering based on importance measurement was implemented. The evolutionary trends of keywords and core themes were analyzed and studied. The method proposed in this study not only considers the keyword importance and automatically identifies core themes, but also provides new data mining methods for thematic document analysis and effectively improves the topic recognition effect in related fields such as journals and other disciplines.
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Received: 08 December 2017
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