AbstractThis study used several informatics techniques to analyze consumer-driven social media data from four cities (Paris, Milan, New York, and London) during the 2019 Fall/Winter (F/W) Fashion Week. Analyzing keywords using a semantic network analysis method revealed the main characteristics of the collections, celebrities, influencers, fashion items, fashion brands, and designers connected with the four fashion weeks. Using topic modeling and a sentiment analysis, this study confirmed that brands that embodied similar themes in terms of topics and had positive sentimental reactions were also most frequently mentioned by the consumers. A semantic network analysis of the tweets showed that social media, influencers, fashion brands, designers, and words related to sustainability and ethics were mentioned in all four cities. In our topic modeling, the classification of the keywords into three topics based on the brand collection’s themes provided the most accurate model. To identify the sentimental evaluation of brands participating in the 2019 F/W Fashion Week, we analyzed the consumers’ sentiments through positive, neutral, and negative reactions. This quantitative analysis of consumer-generated social media data through this study provides insight into useful information enabling fashion brands to improve their marketing strategies.
Fashion informatics of the Big 4 Fashion Weeks using topic modeling and sentiment analysis