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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/24996

Title: Sentiment analysis using KNIME: a systematic literature review of big data logistics
Authors: Graham, Gary
Meriton, Royston Francis
Hennelly, Patrick
Issue Date: 2016
Publisher: © IARIA
Citation: GRAHAM, G., MERITON, R.F. and HENELLY, P., 2016. Sentiment analysis using KNIME: a systematic literature review of big data logistics. Presented at Emerging 2016: The Eighth International Conference on Emerging Networks and Systems Intelligence, Venice, Italy, 9-13th October, pp. 65-8.
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 OM and OR journal articles and government reports. The classification system described here analyses the opinions of the performance of various public and private, manufacturing, medical, service and retail organizations in integrating big data into their logistics. It explains methods of data collection and the sentiment analysis process for classifying big data logistics literature using KNIME. Finally, it then gives an overview of the differences and explores future possibilities in sentiment analysis for investigating different industrial sectors and data sources.
Description: This is a conference paper.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/24996
Publisher Link: https://www.iaria.org/conferences2016/CfPEMERGING16.html
ISBN: 9781612085098
ISSN: 2326-9383
Appears in Collections:Conference Papers and Presentations (Loughborough University London)

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