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| Identifying and Visualizing Emerging Trends in Domain Based on PWLR Model |
| Liu Ziqiang1, Hu Zhengyin2,3, Xu Haiyun2,3, Fang Shu2,3 |
1.School of Journalism and Communication, Nanjing Normal University, Nanjing 210097
2.Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041
3.Department of Library Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 |
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Abstract Exploring and constructing an accurate and effective framework for the analysis of emerging trends is of great significance to the information work of emerging trends judgment and research, public opinion monitoring, and so on. First, Bi-Gram and Tri-Gram, which are multi-lexical features, are extracted based on an N-Gram model, and a piecewise linear regression (PWLR) model then is used to fit these features. In addition, the model is used to detect the emerging multi-lexical features on the recent time line as well as to accurately identify new words with potential for development and identify new words based on the previous step. A hierarchical clustering algorithm is used to identify emerging trends in the field and visualize the results. Through empirical research, the main emerging trends in the field of gene editing are identified as CRISPR-Cas9 technology, gene therapy, and animal and plant gene editing, which verifies the feasibility and validity of the method proposed herein.
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Received: 20 October 2019
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