Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media

Noor Farizah Ibrahim*, Xiaojun Wang

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

52 Citations (Scopus)
399 Downloads (Pure)


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 languageEnglish
Pages (from-to)32-45
Number of pages14
JournalComputers in Human Behavior
Early online date12 Feb 2019
Publication statusPublished - Jul 2019


  • Big data analytics
  • Online retailing
  • Service provision
  • Social media
  • Time series


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