Purpose

The paper aims to explore whether topic analysis (identification of the core contents, trends and topic distribution in the target field) can be performed using a more low-cost and easily applicable method that relies on a small dataset, and how we can obtain this small dataset based on the features of the publications.

Design/methodology/approach

The paper proposes a topic analysis method based on prolific and authoritative researchers (PARs). First, the authors identify PARs in a specific discipline by considering the number of publications and citations of authors. Based on the research publications of PARs (small dataset), the authors then construct a keyword co-occurrence network and perform a topic analysis. Finally, the authors compare the method with the traditional method.

Findings

The authors found that using a small dataset (only 6.47% of the complete dataset in our experiment) for topic analysis yields relatively high-quality and reliable results. The comparison analysis reveals that the proposed method is quite similar to the results of traditional large dataset analysis in terms of publication time distribution, research areas, core keywords and keyword network density.

Research limitations/implications

Expert opinions are needed in determining the parameters of PARs identification algorithm. The proposed method may neglect the publications of junior researchers and its biases should be discussed.

Practical implications

This paper gives a practical way on how to implement disciplinary analysis based on a small dataset, and how to identify this dataset by proposing a PARs-based topic analysis method. The proposed method presents a useful view of the data based on PARs that can produce results comparable to traditional method, and thus will improve the effectiveness and cost of interdisciplinary topic analysis.

Originality/value

This paper proposes a PARs-based topic analysis method and verifies that topic analysis can be performed using a small dataset.

中文翻译：

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基于多产权威研究者的学科主题分析

目的

本文旨在探索是否可以使用一种依赖于小数据集的成本更低且易于应用的方法进行主题分析（识别目标领域的核心内容、趋势和主题分布），以及我们如何获得这一点。基于出版物特征的小数据集。

设计/方法/方法

论文提出了一种基于多产权威研究人员（PARs）的主题分析方法。首先，作者通过考虑作者的出版物数量和引用次数来确定特定学科中的 PAR。基于PARs（小数据集）的研究成果，作者然后构建了一个关键词共现网络并进行了主题分析。最后，作者将该方法与传统方法进行了比较。

发现

作者发现，使用小数据集（在我们的实验中仅占整个数据集的 6.47%）进行主题分析会产生相对高质量和可靠的结果。对比分析表明，该方法在发表时间分布、研究领域、核心关键词和关键词网络密度等方面与传统大数据集分析的结果非常相似。

研究限制/影响

在确定 PAR 识别算法的参数时需要专家意见。所提出的方法可能会忽略初级研究人员的出版物，应讨论其偏见。

实际影响

本文给出了如何基于小数据集进行学科分析的实用方法，以及如何通过提出基于PARs的主题分析方法来识别该数据集。所提出的方法提供了一种基于 PAR 的有用数据视图，可以产生与传统方法相当的结果，从而提高跨学科主题分析的有效性和成本。

原创性/价值

本文提出了一种基于PARs的主题分析方法，并验证了可以使用小数据集进行主题分析。