Projects per year
Abstract
Scientometric methods have long been used to identify technological trajectories, but we have seldom seen reproducible methods that allow for the identification of a technological emergence in a set of documents. This study evaluates the use of three different reproducible approaches for identifying the emergence of technological novelties in scientific publications. The selected approaches are term counting technique, the emergence score (EScore) and Latent Dirichlet Allocation (LDA). We found that the methods provide somewhat distinct perspectives on technological. The term count based method identifies detailed emergence patterns. EScore is a complex bibliometric indicator that provides a holistic view of emergence by considering several parameters, namely term frequency, size, and origin of the research community. LDA traces emergence at the thematic level and provides insights on the linkages between emerging research topics. The results suggest that term counting produces results practical for operational purposes, while LDA offers insight at a strategic level.
Original language | English |
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Pages (from-to) | 215-247 |
Journal | Scientometrics |
Volume | 122 |
Issue number | 1 |
Early online date | 2 Nov 2019 |
DOIs | |
Publication status | Published - 1 Jan 2020 |
MoE publication type | A1 Journal article-refereed |
Funding
Open access funding provided by Technical Research Centre of Finland (VTT). The Academy of Finland research supported this work with the Grant (288609) “Modeling Science and Technology Systems Through Massive Data Collections”. We acknowledge support from the US National Science Foundation (Award #1759960 - “Indicators of Technological Emergence”) to Search Technology, Inc., and Georgia Tech.
Keywords
- Emergence score (EScore)
- Technological emergence
- Text analytics
- Topic modeling
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Dive into the research topics of 'Evaluating technological emergence using text analytics: Two case technologies and three approaches'. Together they form a unique fingerprint.Projects
- 1 Finished
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SA MST: Modeling Science and Technology Systems Through Massive Data Collections
Suominen, A. (Participant)
1/09/15 → 31/08/18
Project: Academy of Finland project