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

作者：

Highlights：

• Explore and decode the sentiment dynamics of Twitter users regarding online retailing brands.

• Synthesizing multiple big data analytical techniques as an integrated approach.

• Time points that lead to significant deviations in sentiment trends.

• Key factors causing dynamic changes in customer sentiment.

• Insights for online retailers on where to target their responses and effective strategies for service improvement.

摘要

•Explore and decode the sentiment dynamics of Twitter users regarding online retailing brands.•Synthesizing multiple big data analytical techniques as an integrated approach.•Time points that lead to significant deviations in sentiment trends.•Key factors causing dynamic changes in customer sentiment.•Insights for online retailers on where to target their responses and effective strategies for service improvement.

论文关键词：Online retailing,Service provision,Time series,Social media,Big data analytics

论文评审过程：Received 24 October 2018, Revised 30 January 2019, Accepted 7 February 2019, Available online 12 February 2019, Version of Record 16 February 2019.

论文官网地址：https://doi.org/10.1016/j.chb.2019.02.004