Bibliographic Computer Science Indexing Review with Disease Covid 19

IJNMT (International Journal of New Media Technology) Pub Date : 2022-07-05 DOI:10.31937/ijnmt.v9i1.2509

Andrianingsih Andrianingsih, Tri Wahyu Widyaningsih, M. A. Dewi

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Abstract

Abstract - Researchers in conducting their research use the search using the homepage of the publication, according to expertise, collaboration in research, and research interests. And at this time the Covid 19 pandemic, became a trending topic for researchers, in various scientific fields. This study classifies based on publications located on the homepage source namely Scopus and Google Scholar, by analyzing the following topics, namely Natural Language Processing, Text Mining, Remote Sensing, and Sentiment Analysis using Name Entity Recognition to detect and classify named entities in text and using occurrence and link strength methods. The results showed science index literature about diseases Covid 19, obtained that Scopus has the most equitable percentage, has a good occurrence and link strength among the five scientific fields, namely Natural Language Processing 23.81%.33%, Text Mining 19.05%%, Remote Sensing 0 %, Sentiment Analysis 57.14 % then Google Scholar Natural Language Processing 51.35%, Text Mining 0 %, Remote Sensing 48.65 %, Sentiment Analysis 0 % Index Terms : Information Extraction; Bibliographic indexing; Disease Covid 19

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Covid - 19疾病的书目计算机科学索引综述

摘要-研究人员在进行研究时，根据专业知识、研究合作和研究兴趣，使用出版物的主页进行搜索。此时，Covid - 19大流行成为各个科学领域研究人员的热门话题。本研究基于位于主页来源即Scopus和Google Scholar上的出版物进行分类，通过分析以下主题，即自然语言处理，文本挖掘，遥感和情感分析，使用名称实体识别来检测和分类文本中的命名实体，并使用出现率和链接强度方法。结果显示，关于Covid - 19疾病的科学索引文献中，Scopus在5个科学领域中占有最公平的比例，发生率和链接强度均较好，即自然语言处理23.81%。33%，文本挖掘19.05%，遥感0%，情感分析57.14%，然后Google Scholar自然语言处理51.35%，文本挖掘0%，遥感48.65%，情感分析0%;书目索引;Covid - 19

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IJNMT (International Journal of New Media Technology)

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