This chapter builds on a dataset where online users of a spa platform participated in an online idea contest providing free-text descriptions of their proposals for spa services. A panel of domain experts annotated these idea descriptions with a score for their innovativeness that serves as ground truth for machine learning experiments. Thus, the contribution lies in the application of topic modeling techniques to free-text idea descriptions in order to automatically identify innovative proposals based on advanced text processing and machine learning. Results of this case study indicate that topic modeling can outperform the ZeroR baseline as well as traditional survey scales for lead user identification and therefore constitute a first step towards exploring this technique for innovation research.
Sottocornola, G., Stella, F., Symeonidis, P., Zanker, M., Krajger, I., Faullant, R., et al. (2019). Identifying innovative idea proposals with topic models-a case study from SPA tourism. In Big Data and Innovation in Tourism, Travel, and Hospitality (pp. 115-133). Springer Singapore [10.1007/978-981-13-6339-9_8].
Identifying innovative idea proposals with topic models-a case study from SPA tourism
Stella, F;
2019
Abstract
This chapter builds on a dataset where online users of a spa platform participated in an online idea contest providing free-text descriptions of their proposals for spa services. A panel of domain experts annotated these idea descriptions with a score for their innovativeness that serves as ground truth for machine learning experiments. Thus, the contribution lies in the application of topic modeling techniques to free-text idea descriptions in order to automatically identify innovative proposals based on advanced text processing and machine learning. Results of this case study indicate that topic modeling can outperform the ZeroR baseline as well as traditional survey scales for lead user identification and therefore constitute a first step towards exploring this technique for innovation research.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.