Abstract
The Twittersphere often offers valuable information about current events. However, despite the enormous quantity of tweets regarding online retailing, we know little about customers’ perceptions regarding the products and services offered by online retail brands. Therefore, this study focuses on analysing brand-related tweets associated with five leading UK online retailers during the most important sales period of the year, covering Black Friday, Christmas and the New Year's sales events. We explore trends in customer tweets by utilising a combination of data analytics approaches including time series analysis, sentiment analysis and topic modelling to analyse the trends of tweet volume and sentiment and to understand the reasons underlying changes in sentiment. Through the sentiment and time series analyses, we identify several critical time points that lead to significant deviations in sentiment trends. We then use a topic modelling approach to examine the tweets in the period leading up to and following these critical moments to understand what exactly drives these changes in sentiment. The study provides a deeper understanding of online retailing customer behaviour and derives significant managerial insights that are useful for improving online retailing service provision.
| Original language | English |
|---|---|
| Pages (from-to) | 32-45 |
| Number of pages | 14 |
| Journal | Computers in Human Behavior |
| Volume | 96 |
| DOIs | |
| Publication status | Published - Jul 2019 |
Bibliographical note
Funding Information:This research is partially funded by the Government of Malaysia .
Publisher Copyright:
© 2019 Elsevier Ltd
Keywords
- Big data analytics
- Online retailing
- Service provision
- Social media
- Time series
ASJC Scopus subject areas
- Arts and Humanities (miscellaneous)
- Human-Computer Interaction
- General Psychology