akc: A Tidy Framework for Automatic Knowledge Classification in R
Tian-Yuan Huang, Li Li, Liying Yang · The R Journal · 2022
Knowledge classification is an extensive and practical approach in domain knowledge management. Automatically extracting and organizing knowledge from unstructured textual data is desirable and appealing in various circumstances. In this paper, the tidy framework for automatic knowledge classification supported by the akc package is introduced. With powerful support from the R ecosystem, the akc framework can handle multiple procedures in data science workflow, including text cleaning, keyword extraction, synonyms consolidation and data presentation. While focusing on bibliometric analysis, the akc package is extensible to be used in other contexts. This paper introduces the framework and its features in detail. Specific examples are given to guide the potential users and developers to participate in open science of text mining.