Classification of intangible social innovation concepts

Nikola Milosevic, Abdullah Gök, Goran Nenadic

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

2 Citations (Scopus)


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)
Number of pages12
ISBN (Print)9783319919461
Publication statusPublished - 13 Jun 2018

Publication series

NameLecture Notes in Computer Science


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


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