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In Search of New Product Ideas: Identifying Ideas in Online Communities by Machine Learning and Text Mining
Author(s) -
Christensen Kasper,
Nørskov Sladjana,
Frederiksen Lars,
Scholderer Joachim
Publication year - 2017
Publication title -
creativity and innovation management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.148
H-Index - 60
eISSN - 1467-8691
pISSN - 0963-1690
DOI - 10.1111/caim.12202
Subject(s) - computer science , exploit , boosting (machine learning) , task (project management) , data science , product (mathematics) , focus (optics) , artificial intelligence , online community , machine learning , information retrieval , natural language processing , world wide web , management , physics , geometry , computer security , mathematics , optics , economics
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.