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dc.contributor.authorKenneth Coshen_US
dc.contributor.authorSakgasit Ramingwongen_US
dc.contributor.authorNarissara Eiamkanitchaten_US
dc.contributor.authorLachana Ramingwongen_US
dc.description.abstract© 2018 IEEE. Understanding the ways that research topics are evolving in a research domain is important when considering research proposals. Bibliometric analysis provides a variety of tools for exploring publication data, but often involves manual effort. This paper presents an automatic method for extracting and examining key research themes by using natural language processing to parse a large collection of papers. The method was applied to over 8,000 papers published in the software engineering field over the past 20 years. Key research themes were identified and visualized, so that trends could be highlighted. Some research fields that are in decline are identified, along with newly popular research topics such as fuzzy set membership, cloud computing, feature selection and agile development teams.en_US
dc.subjectComputer Scienceen_US
dc.titleAutomatically Identifying Themes and Trends in Software Engineering Researchen_US
dc.typeConference Proceedingen_US
article.title.sourcetitle2018 10th International Conference on Knowledge and Smart Technology: Cybernetics in the Next Decades, KST 2018en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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