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.

中文翻译：

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使用主题建模和情感分析的四大时装周的时尚信息学

本研究使用多种信息学技术来分析 2019 年秋冬 (F/W) 时装周期间来自四个城市（巴黎、米兰、纽约和伦敦）的消费者驱动的社交媒体数据。使用语义网络分析方法分析关键词，揭示了与四个时装周相关的系列、名人、影响者、时尚单品、时尚品牌和设计师的主要特征。使用主题建模和情感分析，本研究证实，在主题方面体现相似主题并具有积极情感反应的品牌也是消费者最常提及的品牌。对推文的语义网络分析表明，所有四个城市都提到了社交媒体、影响者、时尚品牌、设计师以及与可持续发展和道德相关的词语。在我们的主题建模中，根据品牌系列的主题将关键词分为三个主题提供了最准确的模型。为了确定参与 2019 秋冬时装周品牌的情感评价，我们通过正面、中性和负面反应分析了消费者的情感。通过这项研究对消费者生成的社交媒体数据进行定量分析，深入了解有助于时尚品牌改进营销策略的有用信息。