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| Detecting Emerging Trends of Funds Based on DTM Model and Text Analytics: Case Study of NSF Graphene Field |
Xu Lulu, Wang Xiaoyue( ), Bai Rujiang, Zhou Yanting |
| Institute of Scientific & Technical Information, Shandong University of Technology, Zibo 255049, China |
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Abstract [Objective] This study tries to extract more semantic information from the science and technology literature, aiming to identify emerging trends from the documents of fund projects. [Methods] First, we proposed a new trend detection method based on the DTM model and text analytics. Then, we identified the topic probability distribution of the fund projects and constructed a new theme detection formula based on the text features. Finally, we detected the emerging trends in the field of NSF graphene. [Results] The proposed method identified emerging trends of fund projects and provided information for technology innovation. [Limitations] We only examined the fund project documents from the perspectives of the amount, length, and theme of funding. [Conclusions] The proposed method could effectively identify emerging trends of fund projects.
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Received: 01 November 2017
Published: 03 April 2018
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