Understanding the research landscape of smart libraries using text mining and data visualization : A use case of Voyant Tool

IF 1.6 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE COLLNET Journal of Scientometrics and Information Management Pub Date : 2023-01-01 DOI:10.47974/cjsim-2022-0051
Sarita Gulati, A. Sinhababu, Prof. Rupak Chakravarty
{"title":"Understanding the research landscape of smart libraries using text mining and data visualization : A use case of Voyant Tool","authors":"Sarita Gulati, A. Sinhababu, Prof. Rupak Chakravarty","doi":"10.47974/cjsim-2022-0051","DOIUrl":null,"url":null,"abstract":"Text mining has become one of the most common methods used to analyze natural language documents today. In this study, 81 open access journal articles on the topic of “smart libraries” from Google Scholar were analyzed using text mining techniques. Articles were chosen based on the criterion that they had to be open access and have smart libraries as their main component. To analyze the articles and discover interesting text patterns within the retrieved articles, Voyant Tool, an open-source text-mining tool, was used. It assists in finding Corpus Collocates that are closely related in texts as well as identifying n-grams in the field of smart libraries (sequences of words occurring together with the context that surrounds them). Results showed that the most frequently used words in the corpus were smart (3533); information (2253), data (1753); technology (1382); service (1327). Furthermore, findings showed that the longest document had 14210 words and the shortest had 1012. Overall, the study findings will help in understand the smart libraries ecosystem with deeper insight.","PeriodicalId":10501,"journal":{"name":"COLLNET Journal of Scientometrics and Information Management","volume":"1 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"COLLNET Journal of Scientometrics and Information Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47974/cjsim-2022-0051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Text mining has become one of the most common methods used to analyze natural language documents today. In this study, 81 open access journal articles on the topic of “smart libraries” from Google Scholar were analyzed using text mining techniques. Articles were chosen based on the criterion that they had to be open access and have smart libraries as their main component. To analyze the articles and discover interesting text patterns within the retrieved articles, Voyant Tool, an open-source text-mining tool, was used. It assists in finding Corpus Collocates that are closely related in texts as well as identifying n-grams in the field of smart libraries (sequences of words occurring together with the context that surrounds them). Results showed that the most frequently used words in the corpus were smart (3533); information (2253), data (1753); technology (1382); service (1327). Furthermore, findings showed that the longest document had 14210 words and the shortest had 1012. Overall, the study findings will help in understand the smart libraries ecosystem with deeper insight.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
理解使用文本挖掘和数据可视化的智能图书馆的研究前景:Voyant工具的一个用例
文本挖掘已经成为当今用于分析自然语言文档的最常用方法之一。本研究使用文本挖掘技术分析了b谷歌Scholar上81篇以“智能图书馆”为主题的开放获取期刊文章。文章的选择标准是它们必须是开放获取的,并且以智能库为主要组成部分。为了分析文章并在检索到的文章中发现有趣的文本模式,我们使用了开源文本挖掘工具Voyant Tool。它有助于查找文本中密切相关的语料库搭配,以及识别智能图书馆领域的n-grams(单词序列与围绕它们的上下文一起出现)。结果表明,语料库中使用频率最高的词是smart(3533个);信息(2253),数据(1753);技术(1382);服务(1327)。此外,调查结果显示,最长的文档有14210个单词,最短的文档有1012个单词。总体而言,研究结果将有助于更深入地了解智能图书馆生态系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
COLLNET Journal of Scientometrics and Information Management
COLLNET Journal of Scientometrics and Information Management INFORMATION SCIENCE & LIBRARY SCIENCE-
自引率
0.00%
发文量
11
期刊最新文献
Mapping of top papers in the subject category of Soil Science Mapping global research on expert systems Research trends in the field of natural language processing : A scientometric study based on global publications during 2001-2020 Classic articles in cervical cancer research : A bibliometric analysis Human and algorithmic decision-making in the personnel selection process: A comparative bibliometric on bias
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1