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Towards Computational Assessment of Idea Novelty

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Title:Towards Computational Assessment of Idea Novelty
Authors:Wang, Kai
Dong, Boxiang
Ma, Junjie
Keywords:Text Mining in Big Data Analytics
Collaboration Systems and Technologies
Computational methods, Creativity, Crowdsourcing, Idea evaluation, Topic modeling
Date Issued:08 Jan 2019
Abstract:In crowdsourcing ideation websites, companies can easily collect large amount of ideas. Screening through such volume of ideas is very costly and challenging, necessitating automatic approaches. It would be particularly useful to automatically evaluate idea novelty since companies commonly seek novel ideas. Three computational approaches were tested, based on Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA) and term frequency–inverse document frequency (TF-IDF), respectively. These three approaches were used on three set of ideas and the computed idea novelty was compared with human expert evaluation. TF-IDF based measure correlated better with expert evaluation than the other two measures. However, our results show that these approaches do not match human judgement well enough to replace it.
Pages/Duration:9 pages
URI:http://hdl.handle.net/10125/59531
ISBN:978-0-9981331-2-6
DOI:10.24251/HICSS.2019.111
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Text Mining in Big Data Analytics


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