Identifying customer knowledge on social media through data analytics. Issue 1 (26th September 2018)

• Record Type: Journal Article
• Authors: He, Wu; Zhang, Weidong; Tian, Xin; Tao, Ran; Akula, Vasudeva
• Is Part Of: Journal of enterprise information management. Volume 32:Issue 1(2019)
• Page Start: 152
• Page End: 169
• Publication Date: 2018-09-26
• Subjects: Social media -- Sentiment analysis -- Analytics -- Customer knowledge; Management information systems -- Periodicals; Business logistics -- Periodicals; Business -- Data processing -- Periodicals; Management -- Data processing -- Periodicals; 658.05
• DOI: 10.1108/JEIM-02-2018-0031
• Languages: English
• ISSNs: 1741-0398

Abstract

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.