The contribution of the lexical component in hybrid clustering, the case of four decades of “Scientometrics”

Bart Thijs, W. Glänzel

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The introduction of textual analysis and the use of lexical similarities already proved an important asset in science mapping. Earlier research showed the added value of hybrid document networks over link-based ones through the reduction of the extreme sparseness. However, it was only after the application of Natural Language Processing and phrase extraction that networks purely based on lexical similarities could be used as input for topic detection in quantitative science studies. This study investigates the contribution of the lexical component in hybrid cluster on a set of articles published in the journal Scientometrics since its foundation during four decades. Shifting the weight of the lexical components generates changes in the structure of the underlying hybrid network, which can be detected through clustering techniques. We show that these changes are not moving documents randomly, but in fact identify small groups of papers either at the borderline between different topics or combining those. In addition, the analysis substantiates that the lexical component adopts the structure of the network rather than amplifies hidden structures of the link-based network.

Original languageEnglish
Pages (from-to)21-33
Number of pages13
JournalScientometrics
Volume115
Issue number1
DOIs
Publication statusPublished - Apr 1 2018

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Keywords

  • Hybrid clustering
  • Lexical similarity
  • Natural Language Processing
  • Science mapping
  • Scientometrics

ASJC Scopus subject areas

  • Social Sciences(all)
  • Computer Science Applications
  • Library and Information Sciences

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