SNS 빅데이터를 활용한 가방(Bag)에 대한 소비자 인식 분석

초록

Given the rapid changes of fashion bags’ trend phenomena, this study aims to investigate consumer perceptions of bags based on associated words with bags using SNS big data which offer immediate and diversified comments from consumers. Text mining technique was used and the text data was collected from blogs, cafe, and Facebook from search engines (Naver, Daum, Google) containing the keyword of ‘bag’ with the period from Jan 1, 2018 to Dec. 31, 2019. The frequency and matrix data of words were extracted by TEXTOM, the social matrix program. NodeXL was used to find the connection structure of words and analyze the degree centrality. The network related to bags was visualized using the NetDraw program. The CONCOR analysis was performed to make a cluster of words based on their similarities. As results, the keyword ‘Price’ showed the highest rank of frequency and degree centrality followed by ‘Cross Bag’, ‘Good’, ‘Pretty’, ‘Backpack’, ‘Tote Bag’, ‘Travel Bag’, ‘Daily Bag’, ‘Purchase’, and ‘Boston Bag’ in the top 10 frequency rankings. Results looking at highly ranked 70 keywords indicated the categories of product attributes, items, brand, and TPO. The results of CONCOR analysis demonstrated four groups including ‘Luxury brand bag’, ‘Trend-seeking daily bag’, ‘Personal lifestyle bag’, and ‘Utility leisure bag’. This study extends the scope of research by using SNS big data and provides directions for product development and marketing strategies based on customer comments and opinions.

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