The study aims at comparing two methods for tracing the temporal evolution of topics and keywords in corpora of scientific literature: the well-known Latent Dirichelet Allocation and a new knowledge-based system that has been developed in a functional data analysis unsupervised perspective. Object of the study is a corpus of abstracts of articles published by the American Journal of Sociology over a century (1921-2018). Our study advocates that the two methods might not be seen as alternative but rather as integrable means to improve the interpretation of findings.
Knowledge discovery for dynamic textual data: temporal patterns of topics and word clusters in corpora of scientific literature
Matilde Trevisani
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2019-01-01
Abstract
The study aims at comparing two methods for tracing the temporal evolution of topics and keywords in corpora of scientific literature: the well-known Latent Dirichelet Allocation and a new knowledge-based system that has been developed in a functional data analysis unsupervised perspective. Object of the study is a corpus of abstracts of articles published by the American Journal of Sociology over a century (1921-2018). Our study advocates that the two methods might not be seen as alternative but rather as integrable means to improve the interpretation of findings.File in questo prodotto:
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