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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title In search of new product ideas: Identifying ideas in online communities by machine learning and text mining
Organization Unit
Authors
  • Kasper Christensen
  • Sladjana Nørskov
  • Lars Frederiksen
  • Joachim Scholderer
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title Creativity and Innovation Management
Publisher Wiley-Blackwell Publishing, Inc.
Geographical Reach international
ISSN 0963-1690
Volume 26
Number 1
Page Range 17 - 30
Date 2017
Abstract Text Online communities are attractive sources of ideas relevant for new product development and innovation. However, making sense of the ‘big data’ in these communities is a complex analytical task. A systematic way of dealing with these data is needed to exploit their potential for boosting companies' innovation performance. We propose a method for analysing online community data with a special focus on identifying ideas. We employ a research design where two human raters classified 3,000 texts extracted from an online community, according to whether the text contained an idea. Among the 3,000, 137 idea texts and 2,666 non-idea texts were identified. The human raters could not agree on the remaining 197 texts. These texts were omitted from the analysis. The remaining 2,803 texts were processed by using text mining techniques and used to train a classification model. We describe how to tune the model and which text mining steps to perform. We conclude that machine learning and text mining can be useful for detecting ideas in online communities. The method can help researchers and firms identify ideas hidden in large amounts of texts. Also, it is interesting in its own right that machine learning can be used to detect ideas.
Digital Object Identifier 10.1111/caim.12202
Other Identification Number merlin-id:14596
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