Identifying customer knowledge on social media through data analytics

Wu He (Old Dominion University, Norfolk, Virginia, USA)
Weidong Zhang (School of Management, Jilin University, Changchun, China)
Xin Tian (Kennesaw State University, Marietta, Georgia, USA)
Ran Tao (School of Computer Science and Technology, Donghua University, Shanghai, China)
Vasudeva Akula (The VOZIQ Company, Reston, Virginia, USA)

Journal of Enterprise Information Management

ISSN: 1741-0398

Publication date: 11 February 2019

Abstract

Purpose

Customer knowledge from social media can become an important organizational asset. The purpose of this paper is to identify useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers from social media data and facilitate social media-based customer knowledge management.

Design/methodology/approach

The authors conducted a case study to analyze people’s online discussion on Twitter regarding laptop brands and manufacturers. After collecting relevant tweets using Twitter search APIs, the authors applied statistical analysis, text mining and sentiment analysis techniques to analyze the social media data set and visualize relevant insights and patterns in order to identify customer knowledge.

Findings

The paper identifies useful insights and knowledge from customers and knowledge about customers from social media data. Furthermore, the paper shows how the authors can use knowledge from customers and knowledge about customers to help companies develop knowledge for customers.

Originality/value

This is an original social media analytics study that discusses how to transform large-scale social media data into useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers.

Keywords

Citation

Wu He, Weidong Zhang, Xin Tian, Ran Tao and Vasudeva Akula (2019) "Identifying customer knowledge on social media through data analytics", Journal of Enterprise Information Management, Vol. 32 No. 1, pp. 152-169

Download as .RIS

DOI

: https://doi.org/10.1108/JEIM-02-2018-0031

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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