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In search of new product ideas: identifying ideas in online communities by machine learning and text mining


Christensen, Kasper; Nørskov, Sladjana; Frederiksen, Lars; Scholderer, Joachim (2017). In search of new product ideas: identifying ideas in online communities by machine learning and text mining. Creativity and Innovation Management, 26(1):17-30.

Abstract

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.

Abstract

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.

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2 citations in Web of Science®
2 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Center for Corporate Responsibility and Sustainability
Dewey Decimal Classification:330 Economics
Language:English
Date:2017
Deposited On:13 Dec 2016 13:47
Last Modified:08 Dec 2017 21:23
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0963-1690
Publisher DOI:https://doi.org/10.1111/caim.12202
Related URLs:http://www.ccrs.uzh.ch (Author)

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