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| Topic Mining and Evolution Analysis of Medical Sci-Tech Reports with TWE Model |
Shen Si1,Li Qinyu1,Ye Yuan1,Sun Hao1( ),Ye Wenhao2 |
1School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094, China
2School of Information Management, Nanjing University, Nanjing 210023, China |
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Abstract [Objective] The paper uses word embedding representation technology to better discover the implicit associations among topics of the medical science and technology reports, aiming to improve the analysis methods for medical topic evolution. [Methods] We adopted the TWE (Topical Word Embeddings) model to analyze the potential semantic association among topics of oncology studies, as well as their evolution. [Results] We found the splitting correlation of topics in 2006 and 2007, as well as the merging correlation of topics in 2011 and 2012. However, these TWE correlation results were not fully reflected in the topic evolution of generated by traditional LDA method. In 2009 and 2010, the results yielded by traditional LDA and word embedding were completely different. [Limitations] Our sample size is limited because we only collected Chinese reports. More research is needed to examine the proposed method with other medical research topics. [Conclusions] The topic mining and evolution analysis based on the word embeddings representation model could highlight the impacts of deep learning on topic association. It provides better results for topic evolution analysis of medical Sci-Tech reports.
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Received: 11 September 2019
Published: 12 April 2021
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| Fund:Natural Science Foundation of Jiangsu Province(BK20190450);National Natural Science Foundation of China(71974094);National Social Science Fund of China(19FTQB015) |
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Corresponding Authors:
Sun Hao
E-mail: 117107010889@njust.edu.cn
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