Evaluating latent content within unstructured text: an analytical methodology based on a temporal network of associated topics

Title: Evaluating latent content within unstructured text: an analytical methodology based on a temporal network of associated topics
Creator: Camilleri, Edwin; Miah, Shah Jahan
Relation: Journal of Big Data Vol. 8, Issue 1, no. 124
Publisher Link: http://dx.doi.org/10.1186/s40537-021-00511-0
Publisher: Springer
Resource Type: journal article
Date: 2021
Description: In this research various concepts from network theory and topic modelling are combined, to provision a temporal network of associated topics. This solution is presented as a step-by-step process to facilitate the evaluation of latent topics from unstructured text, as well as the domain area that textual documents are sourced from. In addition to ensuring shifts and changes in the structural properties of a given corpus are visible, non-stationary classes of cooccurring topics are determined, and trends in topic prevalence, positioning, and association patterns are evaluated over time. The aforementioned capabilities extend the insights fostered from stand-alone topic modelling outputs, by ensuring latent topics are not only identified and summarized, but more systematically interpreted, analysed, and explained, in a transparent and reliable way.
Subject: natural language processing; topic modelling; network theory; text mining
Identifier: http://hdl.handle.net/1959.13/1459289
Identifier: uon:45634
Identifier: ISSN:2196-1115
Language: eng
Reviewed