Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/50145

Automatically Quantifying Customer Need Tweets: Towards a Supervised Machine Learning Approach

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dc.contributor.author Kühl, Niklas
dc.contributor.author Mühlthaler, Marius
dc.contributor.author Goutier, Marc
dc.date.accessioned 2017-12-28T00:56:57Z
dc.date.available 2017-12-28T00:56:57Z
dc.date.issued 2018-01-03
dc.identifier.isbn 978-0-9981331-1-9
dc.identifier.uri http://hdl.handle.net/10125/50145
dc.description.abstract The elicitation of customer needs is an important task for businesses in order to design customer-centric products and services. While there are different approaches available, most lack automation, scalability and monitoring capabilities. In this work, we demonstrate the feasibility to automatically identify and quantify customer needs by training and evaluating on previously-labeled Twitter data. To achieve that, we utilize a supervised machine learning approach. Our results show that the classification performances are statistically superior-”but can be further improved in the future.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Social Information Systems
dc.subject automated need elicitation, customer needs, e-mobility, supervised machine learning, twitter
dc.title Automatically Quantifying Customer Need Tweets: Towards a Supervised Machine Learning Approach
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2018.258
Appears in Collections: Social Information Systems


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