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| Identifying Topic Evolutionary Pathways through Dynamic Semantic Network Analytics |
| Chen Xiang, Huang Lu, Ni Xingxing, Liu Jiarun, Cao Xiaoli, Wang Changtian |
| School of Management and Economics, Beijing Institute of Technology, Beijing 100081 |
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Abstract With the rapid development of science and technology, several disciplines have shown accelerating changes and intensified cross-fusion. In this context, an important problem researchers face is how research topics can be quickly and accurately identified, evolutionary pathways and trends can be tracked, and research frontiers can be subsequently comprehended. This paper therefore proposes a method for the identification of topic evolutionary pathways based on a dynamic network. First, the dynamic keyword network is constructed via introduction of the piecewise linear representation and the Word2Vec model. Second, a community discovery algorithm is used to identify the communities in the dynamic network, and the evolutionary relationship among topics is represented via measurements of the topic similarity between adjacent time intervals. Finally, the topic evolutionary pathway is identified. This study involves empirical analyses in information science. For validation of the methodology, the results obtained via using the piecewise linear representation method are compared with those obtained via the average time-division method and also with the effect of our method with K-means and LDA in topic identification. This study can therefore provide important decision support for researchers and strategic decision-makers to perform research activities aimed at progressing in the field of study.
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Received: 13 August 2020
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