Volltext-Downloads (blau) und Frontdoor-Views (grau)

Application of machine learning in literature reviews: a framework

  • Literature reviews are essential for any scientific work, both as part of a dissertation or as a stand-alone work. Scientists benefit from the fact that more and more literature is available in electronic form, and finding and accessing relevant literature has become more accessible through scientific databases. However, a traditional literature review method is characterized by a highly manual process, while technologies and methods in big data, machine learning, and text mining have advanced. Especially in areas where research streams are rapidly evolving, and topics are becoming more comprehensive, complex, and heterogeneous, it is challenging to provide a holistic overview and identify research gaps manually. Therefore, we have developed a framework that supports the traditional approach of conducting a literature review using machine learning and text mining methods. The framework is particularly suitable in cases where a large amount of literature is available, and a holistic understanding of the research area is needed. The framework consists of several steps in which the critical mind of the scientist is supported by machine learning. The unstructured text data is transformed into a structured form through data preparation realized with text mining, making it applicable for various machine learning techniques. A concrete example in the field of smart cities makes the framework tangible.

Download full text files

  • 4029.pdf
    eng

Export metadata

Additional Services

Share in Twitter Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Author of HS ReutlingenBozkurt, Yusuf; Braun, Reiner; Rossmann, Alexander
URL:https://www.iadisportal.org/ijcsis/papers/2022170105.pdf
URL:https://www.iadisportal.org/ijcsis/
ISSN:1646-3692
Erschienen in:IADIS International journal on computer science and information systems
Publisher:IADIS
Document Type:Article
Language:English
Year of Publication:2022
Tag:clustering; framework; literature review; machine learning; research method; text mining
Volume:17
Issue:1
Page Number:16
First Page:65
Last Page:80
DDC classes:004 Informatik
Open Access?:Nein