A Study of Kansei Demand of Fans in Social Media

Abstract

As the booming of the Internet era people spend more and more time on social media such as Facebook Twitter Tumblr etc How to catch people’s eye is becoming a critical issue for companies and celebrities since it’s an era of distractions In the past if you or your company want to become popular simply spend money on traditional media like newspaper TV commercial Now you need to know audiences’ need then utilize the new social media platform to reach those specific audiences There is another question raised that is how to know the demand of customers (namely audiences)? Most common used methods are conducting a market survey including questionnaires and focus group However it’s not only wasting time but also effort-consuming In this research we combine text mining technique and Kansei engineering to analysis audiences’ demand First we collect data from Facebook Fan Pages including numerical data (number of likes shares comments) and text data (posts’ content) Then extract the topics by using Latent Dirichlet allocation (LDA) Next Experts will give eight pairs of Kansei words that most relevant for the articles After that we conduct semantic differential questionnaire to find the relationship between topics and Kansei words The relationship can be helpful to writers to know the demand of audience Finally we use supervised LDA to predict the popularity of posts

Date of Award	2016 Aug 3
Original language	English
Supervisor	Sheng-Tun Li (Supervisor)