Classification of intangible social innovation concepts

Nikola Milosevic, Abdullah Gök, Goran Nenadic

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publicationNatural Language Processing and Information Systems (NLDB 2018)
PublisherSpringer
Pages407-418
Number of pages12
Volume10859
ISBN (Print)9783319919461
DOIs
Publication statusPublished - 13 Jun 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10859

Keywords

  • text mining
  • classification
  • natural language processing
  • social innovation

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