Determining Kansei Words in Chocolate Product Development Model Design Based on Social Media Trend by Using Key Element Extraction (KEE) Algorithm

Abstract

This research applies Key Element Extraction (KEE) algorithm to identify customer requirement and preference about chocolate product design based on social media trend. The aim of this paper are to determine Kansei words based on customer feeling and emotion that reflected from comments on social media. Kansei words are determined through data acquisition processes, identification of Kansei words, and determination of key element. Data acquisition is done by copying all comments from every social media account of chocolate products. Kansei words are identified manually from the comments that have been copied from social media. Each Kansei words will be analyzed to determine the key element by using Key Element Extraction (KEE) algorithm. The result shows that there are 25 key element or Kansei words as the results of Key Element Extraction (KEE) algorithm that show customer requirement, preference, feeling, and emotion about chocolate product.