Towards computational assessment of idea novelty

Kai Wang, Boxiang Dong, Junjie Ma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings of the 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019
EditorsTung X. Bui
PublisherIEEE Computer Society
Pages912-920
Number of pages9
ISBN (Electronic)9780998133126
StatePublished - 2019
Event52nd Annual Hawaii International Conference on System Sciences, HICSS 2019 - Maui, United States
Duration: 8 Jan 201911 Jan 2019

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume2019-January
ISSN (Print)1530-1605

Conference

Conference52nd Annual Hawaii International Conference on System Sciences, HICSS 2019
Country/TerritoryUnited States
CityMaui
Period8/01/1911/01/19

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