A Comparative Study on R Packages for Text Mining

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A9JDIKLTG" target="_blank" >RIV/00216208:11320/23:9JDIKLTG - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169662041&doi=10.1109%2fACCESS.2023.3310818&partnerID=40&md5=6ddeed3f3f0d5248032ec982eff73fc2" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169662041&doi=10.1109%2fACCESS.2023.3310818&partnerID=40&md5=6ddeed3f3f0d5248032ec982eff73fc2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/access.2023.3310818" target="_blank" >10.1109/access.2023.3310818</a>

Result language

angličtina

Original language name

A Comparative Study on R Packages for Text Mining

Original language description

"The term Text Mining, which is given to the set of techniques used for the extraction, cleaning and processing of the information in texts, has become useful to provide valuable information to other algorithms and widely used with statistical and machine learning methods. By enabling the extraction of useful insights from textual data, Text Mining has become a potent tool in decision-making and knowledge discovery across many areas, including health care, government, education and industry. R is a mature open-source programming environment that has overstepped its initial scope of application for statistical computing and graphics to be used in pretty all the Data Science knowledge Area Groups. The objective of this paper is to present review and benchmarking analysis of packages for text mining techniques with R in computational systems. The paper reviews thirteen different packages comparing them on their execution time and memory used, for which new tests have been specifically designed. The results of this approach have been intended to be used over the most common tasks carried out when analyzing texts, and comparisons included allow R users to know which packages are best for each task and to improve their performance. Text mining package (tm) stands out particularly in Tokenization and Stemming techniques, while fastTextR is the best choice for Topic Modeling and Normalization. Also in the case of the Term Frequency-Inverse Document Frequency (TF-IDF) technique, the textir package is a clear choice. The other packages will depend on whether the technique is applied to a document-term matrix (DTM) or to plain text. In addition, there are packages that perform better in runtime than in memory usage and vice versa, making the choice more difficult. Packages such as udpipe can achieve better results working in parallel. Future works will include the same analysis for parallel computing, hybrid approaches, and novel algorithms. © 2013 IEEE."

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

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Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

"IEEE Access"

ISSN

2169-3536

e-ISSN

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Volume of the periodical

11

Issue of the periodical within the volume

2023

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

99083-99100

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85169662041