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

Han, Liangxiu; Haleem, Muhammad Salman; Sobeih, Tam; Liu, Ying; Soroka, Anthony; Han, Lianghao

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

conference contribution

posted on 2019-03-11, 13:23 authored 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

Version

AM (Accepted Manuscript)

Publication date

2017

Notes

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