Abstract
This 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.
Highlights
When launching a new clothing product, fashion houses, brands, and designers must analyze the latest fashion trends by investigating the changes in consumer behavior, market environment, and fashion information
The research methods used in informatics, such as social network analysis, sentiment analysis, and topic analysis are increasingly receiving greater scholarly attention, especially for analyzing social media content (Hong & Oh, 2016; Jung & Oh, 2016; Lee et al, 2018)
Semantic network analysis by city Based on the frequency of appearance, the top 100 keywords, including “designers,” “brands,” “influencers,” “fashion items,” “designs,” “materials,” and “themes,” were extracted, classified, and visualized through the Clauset–Newman–Moore (CNM) algorithm
Summary
When launching a new clothing product, fashion houses, brands, and designers must analyze the latest fashion trends by investigating the changes in consumer behavior, market environment, and fashion information. Were generally considered incomplete; they are presently valued as new sources of marketing information. Choi et al Fash Text (2021) 8:33 to generate new content, they are valuable platforms for businesses to elicit direct responses from the customers about their products. The survey method is limited because analyzing the information gathered through surveys is extremely timeconsuming and the responses that deviate from the questions have to be excluded (An & Park, 2017). The research methods used in informatics, such as social network analysis, sentiment analysis, and topic analysis are increasingly receiving greater scholarly attention, especially for analyzing social media content (Hong & Oh, 2016; Jung & Oh, 2016; Lee et al, 2018)
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