A review of research on the improvement of topic model based topic evolution analysis methods for scientific literature

Corresponding author: Tang Xiaoli, tang.xiaoli@imicams.ac.cn

DOI: 10.12201/bmr.202305.00016

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  Abstract: Objective/Significance : To sort out the research progress of topic evolution analysis method, improve the effect of topic identification and trend judgment of scientific and technological literature, support literature information service, and point out the direction for future in-depth research and practical application. Method/process : The derivative model of LDA is summarized according to the defects of the topic model, and the corresponding improvement plan is summarized according to the shortcomings of the analysis methods of the topic evolution of the existing scientific literature. Finally, the prospect is put forward according to the limitations of the existing research. Result/Conclusion : At present, many research achievements have been made in the analysis of topic evolution in the subject field, and a relatively mature system has been formed, but there are still some problems. Through comprehensive investigation and sorting out, this paper can provide inspiration and reference for the study of topic evolution.

  Key words: topic model; topic identification; topic evolution; LDA; text mining

  Submit time: 17 May 2023

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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• HUANG Jin-quan, ZHANG Chu, LIU Ling-tao, PAN Wei, ZHAI Ju-ye, LIU Yu-wen. Research on online hospital feature mining based on HSM_LDA model. 2023. doi: 10.12201/bmr.202305.00012

  ruanxuling, liuqi, guo zhiheng, yanjunfeng. Research on prediction model of breast cancer based on LDA and XGBoost algorithm. 2022. doi: 10.12201/bmr.202106.00007

  Lvyanhua, wangkanglong, zhongxiaoyun, Chen Jun Ye. Construction of user portrait model of Internet medical platform based on text mining: A case study of autism diagnosis data. 2023. doi: 10.12201/bmr.202312.00012

  Ziluo LIN, Xuemei YANG, Zhihao GUAN, Shirui YU, Xiaoli TANG. Interdisciplinary Knowledge Exchange in Medical Subject Research Field With Citation Network. 2022. doi: 10.12201/bmr.202210.00009

  LI Yan-hong, 张迅, Huang Hailiang. Research on Frontier Identification of Medical Oncology Research Based on High Quality Literature. 2023. doi: 10.12201/bmr.202312.00010

  gonghao, zhouluojing. Text analysis of internet medical policy in China based on Digital Mining. 2022. doi: 10.12201/bmr.202208.00005

  baixueyan. Analysis of User Reviews of Dingxiang Doctor APP Based on Text Mining. 2021. doi: 10.12201/bmr.202109.00013

  贾明霞. Research trends of Internet health information behavior of users at home and abroad from the perspective of knowledge mapping. 2021. doi: 10.12201/bmr.202101.00014

  Yang Ying, Li Zizheng, Cao Shu, Cui Lei. Analysis of the Procedure of Realization AND the Significance of Bi-Clustering in Medical Text Mining. 2020. doi: 10.12201/bmr.202012.00003

  BIAN JINWEI, Rao keqin. Spatio-temporal evolution of the coordinated development of Eldercare resources and utilization in China: Based on a hierarchical analysis framework.. 2022. doi: 10.12201/bmr.202209.00014

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