Today brand managers and marketing specialists can leverage huge amount of data to reveal patterns and trends in consumer perceptions, monitoring positive or negative associations of brands with respect to desired topics. In this study, we apply the Semantic Brand Score (SBS) indicator to assess brand importance in the fashion industry. To this purpose, we measure and visualize text data using the SBS Business Intelligence App (SBS BI), which relies on methods and tools of text mining and social network analysis. We collected and analyzed about 206,000 tweets that mentioned the fashion brands Fendi, Gucci and Prada, during the period from March 5 to March 12, 2021. From the analysis of the three SBS dimensions - prevalence, diversity and connectivity - we found that Gucci dominated the discourse, with high values of SBS. We use this case study as an example to present a new system for evaluating brand importance and image, through the analysis of (big) textual data.

翻译：今天,品牌经理和营销专家可以利用大量数据来显示消费者观点的格局和趋势,监测在理想主题方面的品牌正或负联系。在本研究中,我们采用语义品牌评分指标来评估时装行业的品牌重要性。为此,我们利用依赖文字挖掘和社会网络分析的方法和工具的SBS商业情报应用程序(SBS BI)衡量和直观文本数据。我们在2021年3月5日至3月12日期间,收集和分析了大约206 000份提及时装品牌Fendi、Gucci和Prada的推文。我们从对三维----流行性、多样性和连通性----的分析中发现Gucci占据了讨论的主导地位,而SBS的数值很高。我们以这一案例研究为例,通过分析(大)文字数据,提出评估品牌重要性和形象的新系统。