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An automated cloud-based big data analytics platform for customer insights

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conference contribution
posted on 11.03.2019 by Liangxiu Han, Muhammad Salman Haleem, Tam Sobeih, Ying Liu, Anthony Soroka, Lianghao Han
Product reviews have a significant influence on strategic decisions for both businesses and customers on what to produce or buy. However, with the availability of large amounts of online information, manual analysis of reviews is costly and time consuming, as well as being subjective and prone to error. In this work, we present an automated scalable cloud-based system to harness big customer reviews on products for customer insights through data pipeline from data acquisition, analysis to visualisation in an efficient way. The experimental evaluation has shown that the proposed system achieves good performance in terms of accuracy and computing time.

Funding

This work forms part of the EPSRC RECODE Consumer Goods, Big Data and Re-Distributed Manufacturing (EP/M017567/1) Redistributed Manufacturing Network.

History

School

  • Science

Department

  • Computer Science

Published in

2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)

Citation

HAN, L. .... et al., 2017. An automated cloud-based big data analytics platform for customer insights. Presented at the 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter, 21-23 June.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publication date

2017

Notes

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISBN

9781538630662

Language

en

Exports