Adding Taxonomies Obtained by Content Clustering to Semantic Social Network Analysis

Autor:	Peter A. Gloor, Kai Fischbach, Hauke Fuehres, Stefan Nann, Jonas Krauss
Rok vydání:	2010
Předmět:	Information retrieval Social network business.industry Computer science computer.software_genre Betweenness centrality Semantic similarity Taxonomy (general) Content (measure theory) Semantic social network Data mining business Cluster analysis computer Social network analysis
Zdroj:	On Collective Intelligence ISBN: 9783642144806
DOI:	10.1007/978-3-642-14481-3_11
Popis:	This paper introduces a novel method to analyze the content of communication in social networks. Content clustering methods are used to extract a taxonomy of concepts from each analyzed communication archive. Those taxonomies are hierarchical categorizations of the concepts discussed in the analyzed communication archives. Concepts are based on terms extracted from the communication’s content. The resulting taxonomy provides insights into the communication not possible through conventional social network analysis.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::95cdeb56f56660f95c29ec798728118d https://doi.org/10.1007/978-3-642-14481-3_11 Zobrazit plný text záznamu