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Advances in Decision Sciences (ADS)

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Published by Asia University, Taiwan; Scientific and Business World

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

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

Title

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

Authors

  • 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)
  • Wing-Keung Wong
    (Department of Finance, Fintech Center, and Big Data Research Center, Asia University, Taiwan)

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

Classification-JEL

J16, K38, M14

Pages

92-110

https://doi.org/10.47654/v25y2021i3p92-110

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ISSN 2090-3359 (Print)
ISSN 2090-3367 (Online)

Asia University, Taiwan

Scientific and Business World

4.7
2023CiteScore
 
86th percentile
Powered by  Scopus
SCImago Journal & Country Rank
Q2 in Scopus
CiteScore 2023 = 4.7
CiteScoreTracker 2024 = 8.5
SNIP 2023 = 0.799
SJR Quartile = Q1
SJR 2024 = 0.814
H-Index = 20

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