TY - JOUR
T1 - Decoding the sentiment dynamics of online retailing customers
T2 - Time series analysis of social media
AU - Ibrahim, Noor Farizah
AU - Wang, Xiaojun
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
KW - Big data analytics
KW - Online retailing
KW - Service provision
KW - Social media
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=85062237355&partnerID=8YFLogxK
U2 - 10.1016/j.chb.2019.02.004
DO - 10.1016/j.chb.2019.02.004
M3 - Article (Academic Journal)
AN - SCOPUS:85062237355
SN - 0747-5632
VL - 96
SP - 32
EP - 45
JO - Computers in Human Behavior
JF - Computers in Human Behavior
ER -