Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
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Clustering Corporate Brands based on Opinion Mining: A Case Study of the Automobile Industry

오피니언 마이닝을 통한 브랜드 클러스터링: 자동차 산업 사례연구

  • Received : 2016.07.29
  • Accepted : 2016.11.10
  • Published : 2016.11.30
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Abstract

Since the Internet provides a way of expressing and sharing Internet users' mindsets, corporate marketers want to acquire measurable and actionable insights from web data. In the past, companies used to analyze the attitude, satisfaction, and loyalty of consumers toward their brands using survey data, whereas nowadays this is done using the big data extracted from Social Network Services. In this study, we propose a framework for clustering brand names using the social metrics gathered on social media. We also conduct a case study of the automobile industry to verify the feasibility of the proposed framework. We calculate the brand name distance for each pair of brand names based on the total number of times that they are mentioned together. These distances are used to project the brand name onto a 3-dimensional space using multidimensional scaling. After the projection, we found the clusters of brand names and identified the characteristics of each cluster. Furthermore, we concluded this paper with a discussion of the limitations and future directions of this research.

인터넷의 등장으로 고객들은 자신의 의견이나 생각을 인터넷 공간에서 다른 사람들과 공유하기 시작하였다. 이에 따라 기업은 인터넷에서 수집된 데이터를 이용하여 기업에 활용할 수 있는 유의미한 결과를 찾으려는 노력을 하고 있다. 과거 설문조사를 기반으로 고객의 브랜드에 대한 태도나 만족도, 충성도 등을 분석하던 방식에서 소셜 네트워크 서비스(Social Network Service) 등에서 추출된 빅데이터를 이용하여 분석하려는 시도가 이루어지고 있다. 본 연구에서는 SNS에서 수집된 결과를 활용하여 브랜드간의 군집을 발견하는 프레임워크를 제시하고자 한다. 또한 제시된 프레임워크의 실무적용 가능성을 살펴보기 위해 자동차 산업에 대한 사례연구를 수행하였다. 두 개의 브랜드 이름이 소셜 미디어에서 동시에 언급되는 빈도가 높을수록 고객이 두 브랜드를 유사하게 인식한다는 가정 하에 자동차 브랜드 사이의 유사성을 측정하고 거리의 개념으로 변화한 후 다차원 척도법을 이용하여 3차원 상에 표시하였다. 또한 자동차 브랜드에 대한 고객의 인식을 파악하기 위해 유사한 브랜드간의 군집을 도출하고 각 군집을 특징을 기술하였다. 아울러 연구의 한계점과 향후 연구방향을 제시하였다.

Keywords

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