Analyzing the trend of O2O commerce by bilingual text mining on social media

Chien wen Shen, Chen Min Chen, Chiao chen Wang

Research output: Contribution to journalArticlepeer-review

96 Scopus citations

Abstract

Social media has been changing not only the way people communicate with friends, but also the way providers communicate with consumers. Being as the popular means of information acquisition and facilitating the exploration of various topics and market trends, social media was also playing a key role in the development of online to offline (O2O) commerce. In the current study, bilingual text mining was conducted to analyze the trends of O2O commerce from the perspective of social media. Keywords and their concept linking diagrams were first identified to understand the important O2O trends in different languages. While keywords “China”, “Alibaba”, “Baidu”, “DianPing”, “Meituan”, “Asia”, “ecommerce” and “service” were the most strongly associated English terms with O2O, keywords “Baidu”, “Tencent”, “headlines”, “Sina.com”, mode”, “ecommerce”, “retail” and “platform” were closely related to the Chinese tweets. To further understand the similarities and differences between English tweets and Chinese tweets, we compared the bilingual text mining results according to the categories of company, region, service Apps and operation model. Our study provided important insights from crowd intelligence and revealed trends analysis about the recent O2O development in different language regions.

Original languageEnglish
Pages (from-to)474-483
Number of pages10
JournalComputers in Human Behavior
Volume101
DOIs
StatePublished - Dec 2019

Keywords

  • Bilingual text mining
  • Ecommerce
  • Online to offline
  • Social media

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