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Classification of Intangible Social Innovation Concepts

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10859)

Abstract

In social sciences, similarly to other fields, there is exponential growth of literature and textual data that people are no more able to cope with in a systematic manner. In many areas there is a need to catalogue knowledge and phenomena in a certain area. However, social science concepts and phenomena are complex and in many cases there is a dispute in the field between conflicting definitions. In this paper we present a method that catalogues a complex and disputed concept of social innovation by applying text mining and machine learning techniques. Recognition of social innovations is performed by decomposing a definitions into several more specific criteria (social objectives, social actor interactions, outputs and innovativeness). For each of these criteria, a machine learning-based classifier is created that checks whether certain text satisfies given criteria. The criteria can be successfully classified with an F1-score of 0.83–0.86. The presented method is flexible, since it allows combining criteria in a later stage in order to build and analyse the definition of choice.

Keywords

Text mining Classification Natural language processing Social innovation 

Notes

Acknowledgements

The work presented in this paper is a part of KNOWMAK project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grand agreement No. 726992.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Manchester Institute for Innovation ResearchUniversity of ManchesterManchesterUK
  2. 2.School of Computer ScienceUniversity of ManchesterManchesterUK

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