Title
Topic Modeling Of Small Sequential Documents: Proposed Experiments For Detecting Terror Attacks
Keywords
computer aided analysis; machine learning; national security
Abstract
Research is proposed for improving the human-interpretability of topic models; specifically, for topic models of small sequential documents. Experiments are proposed for evaluating the usefulness of topic modeling. The proposed experiments will model the topics of a diverse set of social media content and attempt to correlate the presence of topics related to terror attacks with actual attacks; additionally, correlations between terror attacks and other topics will be checked for.
Publication Date
11-15-2016
Publication Title
IEEE International Conference on Intelligence and Security Informatics: Cybersecurity and Big Data, ISI 2016
Number of Pages
310-312
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/ISI.2016.7745497
Copyright Status
Unknown
Socpus ID
85003914587 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85003914587
STARS Citation
Jones, Brandon W. and Chung, Wingyan, "Topic Modeling Of Small Sequential Documents: Proposed Experiments For Detecting Terror Attacks" (2016). Scopus Export 2015-2019. 4379.
https://stars.library.ucf.edu/scopus2015/4379