Start Date
16-8-2018 12:00 AM
Description
This research aims to automate the time-consuming activity of literature reviews for the collection of indicators used in the assessment of constructs. The study contends that extraction of those potential indicators can be done through text mining techniques developed under specific semantic rules. Authors show their text mining semantic rules for the extraction of potential Digital Forensic Readiness (DFR) indicators from the literature. DFR is a construct of increasing interest in cybersecurity for which no commonly accepted assessment framework exists (DÃaz López 2017), making it a perfect study case.
Recommended Citation
Diaz Lopez, Andres; Xue, Yuan; and Guo, Xiang, "Text Mining for Factors" (2018). AMCIS 2018 Proceedings. 33.
https://aisel.aisnet.org/amcis2018/TREOsPDS/Presentations/33
Text Mining for Factors
This research aims to automate the time-consuming activity of literature reviews for the collection of indicators used in the assessment of constructs. The study contends that extraction of those potential indicators can be done through text mining techniques developed under specific semantic rules. Authors show their text mining semantic rules for the extraction of potential Digital Forensic Readiness (DFR) indicators from the literature. DFR is a construct of increasing interest in cybersecurity for which no commonly accepted assessment framework exists (DÃaz López 2017), making it a perfect study case.