Abstract
GOT is a Python3 software toolkit for taxonomic content analysis of text collections. The structure of the toolkit follows an in-house methodology for processing a collection of texts using a domain taxonomy. The method includes the following steps: (1) computing matrix of relevance between texts and taxonomy leaf topics using a purely structural string-to-text relevance measure based on suffix trees representing the texts and annotated by substring frequencies, (2) obtaining fuzzy clusters of taxonomy leaf topics using a method involving both additive and spectral properties, and (3) finding most specific generalizations of the fuzzy clusters in a rooted tree of the taxonomy. Such a generalization parsimoniously lifts a cluster to its “head subject” in the higher ranks of the taxonomy, to tightly cover the cluster by minimizing the number of errors, “gaps” and “offshoots”. The efficiency of this methodology was illustrated in the analysis of research tendencies in Data Science: our findings led to insights on the tendencies of research that could not be derived by using more conventional techniques. The toolkit can be used either as a whole or with its individual modules including a visualization module. GOT toolkit provides for two usage scenarios: (a) console mode for using via command line and (b) import mode for using in Python3 source codes.
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Frolov, D., Mirkin, B. (2021). GOT: Generalization over Taxonomies, a Software Toolkit for Content Analysis with Taxonomies. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies . WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham. https://doi.org/10.1007/978-3-030-72651-5_49
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