Topics modeling in computer science articles
收藏IEEE2020-09-05 更新2026-04-17 收录
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https://ieee-dataport.org/documents/topics-modeling-computer-science-articles
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资源简介：
By querying open data of notorious scientific databases via representational state transfers, and subsequently enforcing data management practices with a dynamic topic modeling approach on the referred metadata available, this work achieves a feasible form of article set analysis and classification. Research trends for a given field in specific moments are identified, and also the referred trends evolution throughout the years. It is then possible to detect the associated lexical variation overtime on published content, ultimately determining the so-called hot topics in arbitrary instants, including now. Three prominent scientific articles databases are probed by this work, they are arXiv, IEEExplore, and Springer Nature.The dataset contains:Identification of the articles used in the studyThe proportion of the topics in each documentNumber of articles per year per topicDistribution of the words that make up each topic
提供机构：
Melendez Barros, Jose; Pizzigatti Corrêa, Pedro Luiz; de Bona, Glauber; Simplicio Jr, Marcos Antonio; Barbado Júnior, Márcio; Encinas Quille, Rosa V.
创建时间：
2020-09-05
