Application of text mining in PTT forum in analysis of consumer preference for online shopping platforms


  • Wen Ni Shih 0932767304
  • Yu-sen Lin



Big data analysis, Online Shopping, Internet platform, Text mining


  With the advent of economic development and Internet technology, offline retail stores have gradually shifted to virtual shopping networks, and consumers' online shopping has become increasingly prosperous. Since the COVID-19 pandemic, the public has taken the initiative to reduce the number of outdoor activities, which has led to an increase in consumers' willingness to shop online.

  This research takes Shopee, PChome, and MOMO online platforms as the research subjects. Through the PTT forum in 2020 ~ 2021, the articles on e-shopping and lifeismoney boards, using web text crawling analysis, R data text mining and positive/negative sentiment analysis, word cloud charts are used to determine popular keywords related to online shopping issues, and consumers' preferences for online shopping platforms are studied.

  The results show that "seller", "problem" and "offer" are the most discussed keywords indicating that people care about the consumer experience to a certain extent. Through term frequency analysis, we can understand the trends and suggestions brought by popular keywords of online shopping to consumers and online store sellers, and also allow online store sellers to analyze the key decision concerns and the possibility of customers' behavior.

KeywordsBig data analysisOnline ShoppingInternet platformText mining