| Prophet预测-修正的主题强度演化模型——以干细胞领域为实证 |
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| 引用本文： | 张鑫,文奕,许海云,刘忠禹.Prophet预测-修正的主题强度演化模型——以干细胞领域为实证[J].图书情报工作,2020(8):78-92. |
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| 作者姓名： | 张鑫 文奕 许海云 刘忠禹 |
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| 作者单位： | 中国科学院成都文献情报中心 |
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| 基金项目： | 国家自然科学基金项目“基于科学-技术主题关联分析的创新演化路径识别方法研究”(项目编号:71704170);中国科学院信息化专项“面向干细胞领域知识发现的科研信息化应用”(项目编号:XXH13506-203)研究成果之一。 |
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| 摘 要： | 目的/意义]主题演化对科技前沿探测、创新战略部署具有十分重要的作用。方法/过程]将主题演化分析过程分解为主题的表示、相似性关联和强度演化计算几个步骤,提出一种主题强度演化与预测模型,使用LDA模型进行主题的表示,提出内容、共现和趋势相似度等维度进行主题关联计算,引入基于Prophet的预测-修正模型进行主题演化趋势预测。并以干细胞领域为例,进行演化的实证分析。结果/结论]实验表明,对每个研究主题采用Logistic增长模型进行预测R2Score都达到0.90以上,表明Prophet的Logistic增长模型与该领域主题增长趋势规律相符合,能够较好地拟合主题强度的演化趋势。提出的主题演化模型对专业领域内主题分布与演化分析有一定的借鉴意义。
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| 关 键 词： | 主题演化 主题相似性 时间序列 PROPHET |
Prophet Prediction-Correction Topic Evolution Model--A Case Study in Stem Cell Field |
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| Zhang Xin,Wen Yi,Xu Haiyun,Liu Zhongyu.Prophet Prediction-Correction Topic Evolution Model--A Case Study in Stem Cell Field[J].Library and Information Service,2020(8):78-92. |
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| Authors: | Zhang Xin Wen Yi Xu Haiyun Liu Zhongyu |
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| Institution: | (Chengdu Library and Information Center,Chinese Academy of Sciences,Chengdu 610041) |
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| Abstract: | Purpose/significance] Topic evolution analysis plays an important role in detection the technology frontier detection and innovation strategy deployment.Method/process] In this paper,the topic evolution analysis process was divided into several steps:topic representation,similarity correlation and intensity evolution calculation.The LDA model was used to represent the topic;content,co-occurrence,and trend similarity were proposed for topic correlation calculations,and the prophet-based pre-train fine-tuning model was used to predict the topic trends.An empirical analysis was conducted using the stem cell field as an example.Result/conclusion] Experiments show that the Logistic growth model has a R2Score of more than 0.90 for each topic.It shows that the Logistic growth model in Prophet is consistent with the growth trend of topics,and can fit the evolution trend of the topic intensity.The topic evolution model proposed in this paper has certain reference to topic distribution and evolution analysis in specific fields. |
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| Keywords: | topic evolution topic similarity time series Prophet |
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