1672-8505

CN 51-1675/C

毕奕侃,韩毅. 关键词时间分布特征视角下的研究前沿探测研究[J]. 西华大学学报(哲学社会科学版),2020,39(2):105 − 114 . doi: 10.12189/j.issn.1672-8505.2020.02.013
引用本文: 毕奕侃,韩毅. 关键词时间分布特征视角下的研究前沿探测研究[J]. 西华大学学报(哲学社会科学版),2020,39(2):105 − 114 . doi: 10.12189/j.issn.1672-8505.2020.02.013
BI Yi-kan, HAN Yi. Studies on Research Front Detection under the Context of Time Distribution Characteristics of Key Words[J]. Journal of Xihua University (Philosophy & Social Sciences) , 2020, 39(2): 105-114. DOI: 10.12189/j.issn.1672-8505.2020.02.013
Citation: BI Yi-kan, HAN Yi. Studies on Research Front Detection under the Context of Time Distribution Characteristics of Key Words[J]. Journal of Xihua University (Philosophy & Social Sciences) , 2020, 39(2): 105-114. DOI: 10.12189/j.issn.1672-8505.2020.02.013

关键词时间分布特征视角下的研究前沿探测研究

Studies on Research Front Detection under the Context of Time Distribution Characteristics of Key Words

  • 摘要: 关键词是研究前沿探测的重要载体,但单纯词频方法往往忽略统计时间窗口的关键词时间分布特征。文章以2008—2018年CSSCI收录的统计学领域研究论文关键词为对象,以整个时间窗口的样本数据拟合关键词累积分布函数,以词频累积速度和累积词频加速度表征领域关键词的热度和潜力,综合热度和潜力两个维度探测研究前沿及其动态演化。结果表明,该方法较为全面地保留了关键词的原始分布特征,能更加精细化地刻画关键词演化过程,与单纯词频方法相比具有明显差异,在方法上是对具有时间累积性评价的有益探索。

     

    Abstract: It’s of great importance to detect research front under the context of utilizing key words, but the method based on the word-frequency usually ignore the distribution characteristics of the key-word in the time window. The key words included in articles during 2008 to 2018 in the field of statistics recorded in CSSCI are used as empirical data to fit the cumulative distribution function. Based on the cumulative distribution function, two indicators, which are heat and potential degree for key words, are designed to present cumulative velocity and cumulative acceleration for key word frequency. The integration of the two indicators, heat and potential degree, are used to explore the research front and its dynamic evolution. The result found that the new method totally retains the original distribution characteristics of keywords and describes the evolution process of keywords more precisely. Compared with the method based on the word-frequency, the new method can get different results, which is a useful trial for the temporal cumulative evaluation.

     

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