2016
DOI: 10.1111/caim.12202
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In Search of New Product Ideas: Identifying Ideas in Online Communities by Machine Learning and Text Mining

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 … Show more

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Cited by 84 publications
(64 citation statements)
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“…Using text analytics and machine learning methods, Christensen, Nørskov, et al (2017) develop such an algorithm and demonstrate its classification performance and efficiency for the case of extracting new product ideas from an online community related to Lego. show that the same principles can be applied to extract ideas for innovations from a community related to craft brewing.…”
Section: Automatic Idea Detectionmentioning
confidence: 99%
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“…Using text analytics and machine learning methods, Christensen, Nørskov, et al (2017) develop such an algorithm and demonstrate its classification performance and efficiency for the case of extracting new product ideas from an online community related to Lego. show that the same principles can be applied to extract ideas for innovations from a community related to craft brewing.…”
Section: Automatic Idea Detectionmentioning
confidence: 99%
“…We believe that the method introduced by Christensen, Nørskov, et al (2017) shows potential for aiding firms in their search for innovative ideas and may thus serve as a tool for extending the boundaries of inbound open innovation. Still, some questions must be addressed before such a method can be implemented in a firm's innovation processes.…”
Section: Aims Of the Studymentioning
confidence: 99%
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