Brand Clustering Based on Social Big Data: A Case Study

Abstract

Since the Internet provides a common way for expressing and sharing people’s ideas or minds, corporate marketers can gather data on the web to acquire measurable and actionable insights. As the smartphones are widely spread as hand-held devices, SNSs(Social Network Services) in the smartphones become common media to record people’s daily activities and thoughts, researchers try to gather and read people’s mind on SNS through opinion mining or sentiment analysis. In this study we suggest a framework for clustering brand names and perform a case study of cosmetic products using social big data gathered on the social media Microblog, Twitter. To cluster the brand names, we calculate the distance of paired brand names based on the total number paired brand names mentioned together. To identify the clusters among these brands names, we projected the brand names onto a 2dimensional and a 3-dimentional space using MDS(Multi-Dimensional Scaling). After projecting the brand names, we found the clusters of the brand names using k-means clustering and identified the characteristics of each cluster.