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| Topic Mining and Dynamic Evolution Analysis of Funding Projects: Case Studies of AI Field in NSF Data |
| Jin Jialin1, Wang Yuefen1,2,3, Ba Zhichao4, Cen Yonghua2,3 |
1.School of Economics & Management, Nanjing University of Science & Technology, Nanjing 210094
2.Management School of Tianjin Normal University, Tianjin 300387
3.Institute for Big Data Science, Tianjin Normal University, Tianjin 300387
4.Laboratory of Data Intelligence and Interdisciplinary Innovation, Nanjing University, Nanjing 210023 |
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Abstract This study aims to construct an analysis process of topic mining and dynamic evolution for funding projects data. By modeling and mining the relationship between funding projects and the directorate using title and abstract metadata, this proposed process can locate the characteristics of topics, the scope and focus of the directorate, the developing direction, and the evolution of a certain funding field from the perspective of funding project content. Firstly, keywords are extracted from the title and abstract of the funding projects using the rapid automatic keyword extraction (RAKE) algorithm, and the core keywords are obtained through the term segmentation method. Subsequently, the word vector is modeled with the core keywords using the Google word2vec deep learning tool, and the word vector is clustered to mine the topics through the k-means algorithm. Finally, the distribution of topics is described and the similarity between the topics is calculated using the word mover's distance (WMD) algorithm for analyzing the evolutionary trend and the primary evolutionary path of the topics. Using artificial intelligence (AI) in the National Science Foundation (NSF) data, it is discovered that the proposed process can recognize the topics within the AI field and the specific focus of the different directorates. Moreover, through the proposed process, it is observed that the evolution of these topics presents a complex situation of a large number of division and integration, a clear evolutionary path, a prominent focus. and a key evolutionary path through the evolutionary intensity of topics, which indicate that this process can reveal the funding direction for integrating and promoting the related technology in certain fields and can provide strong support for academic research and government planning.
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Received: 22 July 2021
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