Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-91947-8_42 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Text mining,Classification,Natural language processing,Social innovation | Data science,Social innovation,Computer science,Natural language processing,Artificial intelligence,Classifier (linguistics) | Conference |
Volume | ISSN | Citations |
10859 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikola Milosevic | 1 | 31 | 2.38 |
| Abdullah Gök | 2 | 13 | 1.76 |
| Goran Nenadic | 3 | 228 | 13.18 |