P. A. Lobanova, I. F. Kuzminov, E. Yu. Karatetskaia, E. A. Sabidaeva, V. V. Anpilogov
{"title":"Trend Detection Using NLP as a Mechanism of Decision Support","authors":"P. A. Lobanova, I. F. Kuzminov, E. Yu. Karatetskaia, E. A. Sabidaeva, V. V. Anpilogov","doi":"10.3103/s0147688223050106","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The purpose of this article is to present the principles of a developed algorithm for identifying trends based on the analysis of big text data and presenting the result in formats that are convenient for decision makers to be implemented in the iFORA Big Data Mining System. The paper provides an overview of existing text analytics algorithms; outlines the mathematical basis for identifying terms that mean trends, which is proposed and tested for dozens of implemented projects; describes approaches to clustering terms based on their vectors in the Word2vec space; and provides examples of two key visualizations (semantic, trend maps) that outline the range of topics and trends that characterize a particular area of study, as a way to adapt the results of the analysis to the tasks of decision makers. The limitations and advantages of using the proposed approach for decision support are discussed, and directions for future research are suggested.</p>","PeriodicalId":43962,"journal":{"name":"Scientific and Technical Information Processing","volume":"99 1 1","pages":""},"PeriodicalIF":0.4000,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific and Technical Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3103/s0147688223050106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
The purpose of this article is to present the principles of a developed algorithm for identifying trends based on the analysis of big text data and presenting the result in formats that are convenient for decision makers to be implemented in the iFORA Big Data Mining System. The paper provides an overview of existing text analytics algorithms; outlines the mathematical basis for identifying terms that mean trends, which is proposed and tested for dozens of implemented projects; describes approaches to clustering terms based on their vectors in the Word2vec space; and provides examples of two key visualizations (semantic, trend maps) that outline the range of topics and trends that characterize a particular area of study, as a way to adapt the results of the analysis to the tasks of decision makers. The limitations and advantages of using the proposed approach for decision support are discussed, and directions for future research are suggested.
期刊介绍：
Scientific and Technical Information Processing is a refereed journal that covers all aspects of management and use of information technology in libraries and archives, information centres, and the information industry in general. Emphasis is on practical applications of new technologies and techniques for information analysis and processing.
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