뉴스 텍스트를 활용한 국내 자동차 시장의 브랜드 속성 기반 시장 구조 분석

We propose an approach to explore the discourse generated by the public press to draw meaningful insights on brands, brand attributes, and the brand attributes-based market structure. Focusing on the Korean automobile market, we collect 346,795 news articles related to the automobile industry. Then, we employ a machine learning-based text mini ng model to extract the semantic structure of brands and brand attributes reflected in the news text data. Specifically, we utilize the Word2vec model to assign each word as a vector, and measure the semantic (dis)similarity as the vector distance between word vectors. This approach enables us to find key brand attributes in the Korean automobile market, such as “quality”, “luxury”, and “trust”, and to measure the strength of their associations with automobile brands. Using the associations between brand and brand attributes, we visualize the brand-level market structure via perceptual maps that reveal the competitive nature of the market at the brand level. Based on these results, we summarize meaningful insights for the brand management and marketing strategy.