A Detailed Guide on How to Use Statistical Software R for Text Mining

Kim-Hung Pho, Ngoc-Hien Nguyen, Huu-Nhan Huynh and Wing-Keung Wong
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Kim-Hung Pho: Fractional Calculus, Optimization and Algebra Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Ngoc-Hien Nguyen: Design Innovation Center (DBZ), Faculty of Engineering, Mondragon University, Spain
Huu-Nhan Huynh: Department of Mathematics and Informatics, Vietnam Aviation Academy, Ho Chi Minh City, Vietnam

Advances in Decision Sciences, 2021, vol. 25, issue 3, 92-110

Abstract: Text mining is a very important issue in Statistics, Applied Mathematics, and many other areas in Sciences, Engineering, and Business because its applications are extremely rich and varied. Text mining can help academics and practitioners with some specific issues such as spam filtering, personal background matching, sentiment analysis, document classification, etc. The statistical software R is an exceedingly widely used software in Science because of its outstanding and completely free features. To contribute to the literature related to text mining, this study provides detailed instructions on how to use the statistical software R for text mining. To implement this goal, we first introduce the algorithm for text mining. We then discuss how to use the software R to approach each step of the algorithm in detail. As an application, the proposed algorithm is studied with an actual data set. The results found in this study will help academics and practitioners understand how to use the statistical software R to analyze text mining. This paper is very useful for both academics and practitioners in the study of text mining.

Keywords: Guide; Text Mining; Statistics; software R (search for similar items in EconPapers)
JEL-codes: J16 K38 M14 (search for similar items in EconPapers)
Date: 2021
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