Graham, G orcid.org/0000-0002-9908-4974 and Meriton, R (2016) Sentiment Analysis using KNIME: a Systematic Literature Review of Big Data Logistics. In: Proceedings of the Eighth International Conference on Emerging Networks and Systems Intelligence. EMERGING 2016: The Eighth International Conference on Emerging Networks and Systems Intelligence, 09 Oct - 13 Sep 2016, Venice, Italy. International Academy, Research, and Industry Association , pp. 96-99. ISBN 9781510830899
Abstract
Text analytics and sentiment analysis can help researchers to derive potentially valuable thematic and narrative insights from text-based content, such as industry reviews, leading operations management (OM) and operations research (OR) journal articles and government reports. The classification system described here analyses the aggregated opinions of the performance of various public and private, medical, manufacturing, service and retail organizations in integrating big data into their logistics. Although our results show a promising high level of model accuracy, we also suggest caution that the performance of the solution should be compared in terms of the performance of other solutions. This work explains methods of data collection and the sentiment analysis process for classifying big data logistics literature using KNIME (Konstanz Information Miner). Finally, it explores the potential of text mining to build more rigorous and unbiased models of operations management.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Big data; logistics; sentiment analysis; KNIME; text analytics |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Management Division (LUBS) (Leeds) > Logistics, Info, Ops and Networks (LION) (LUBS) |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Sep 2016 10:52 |
Last Modified: | 08 Jul 2019 14:07 |
Published Version: | https://www.iaria.org/conferences2016/EMERGING16.h... |
Status: | Published |
Publisher: | International Academy, Research, and Industry Association |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:104712 |