A Bimodal Network Approach to Model Topic Dynamics

Luigi Di Caro, Marco Guerzoni (marco.guerzoni@unimib.it), Massimiliano Nuccio (massimiliano.nuccio@unive.it) and Giovanni Siragusa

Papers from arXiv.org

Abstract: This paper presents an intertemporal bimodal network to analyze the evolution of the semantic content of a scientific field within the framework of topic modeling, namely using the Latent Dirichlet Allocation (LDA). The main contribution is the conceptualization of the topic dynamics and its formalization and codification into an algorithm. To benchmark the effectiveness of this approach, we propose three indexes which track the transformation of topics over time, their rate of birth and death, and the novelty of their content. Applying the LDA, we test the algorithm both on a controlled experiment and on a corpus of several thousands of scientific papers over a period of more than 100 years which account for the history of the economic thought.

Date: 2017-09
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://arxiv.org/pdf/1709.09373 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1709.09373

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators (help@arxiv.org).