NSL OpenIR  > 中国科学院文献情报中心(北京)
Topic Evolution and Emerging Topic Analysis Based on Open Source Software
Xiang Shen1; Li Wang1,2
2020
Source PublicationJournal of Data and Information Science
Volume5Issue:4Pages:126–136
Abstract

Purpose: We present an analytical, open source and flexible natural language processing and text mining method for topic evolution, emerging topic detection and research trend forecasting for all kinds of data-tagged text.
Design/methodology/approach: We make full use of the functions provided by the open source VOSviewer and Microsoft Office, including a thesaurus for data clean-up and a LOOKUP function for comparative analysis.
Findings: Through application and verification in the domain of perovskite solar cells research, this method proves to be effective.
Research limitations: A certain amount of manual data processing and a specific research domain background are required for better, more illustrative analysis results. Adequate time for analysis is also necessary.
Practical implications: We try to set up an easy, useful, and flexible interdisciplinary text analyzing procedure for researchers, especially those without solid computer programming skills or who cannot easily access complex software. This procedure can also serve as a wonderful example for teaching information literacy.
Originality/value: This text analysis approach has not been reported before.

KeywordTopic Evolution Emerging Topics Text Mining Thesaurus Vosviewer
Indexed ByCSCD
Language英语
CSCD IDCSCD:6857861
Citation statistics
Document Type期刊论文
Identifierhttp://ir.las.ac.cn/handle/12502/11552
Collection中国科学院文献情报中心(北京)
Corresponding AuthorXiang Shen
Affiliation1.National Science Library, Chinese Academy of Sciences
2.Department of Library, Information and Archives Management of University of Chinese Academy of Sciences
First Author Affilication中国科学院文献情报中心
Corresponding Author Affilication中国科学院文献情报中心
Recommended Citation
GB/T 7714
Xiang Shen,Li Wang. Topic Evolution and Emerging Topic Analysis Based on Open Source Software[J]. Journal of Data and Information Science,2020,5(4):126–136.
APA Xiang Shen,&Li Wang.(2020).Topic Evolution and Emerging Topic Analysis Based on Open Source Software.Journal of Data and Information Science,5(4),126–136.
MLA Xiang Shen,et al."Topic Evolution and Emerging Topic Analysis Based on Open Source Software".Journal of Data and Information Science 5.4(2020):126–136.
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