A new system for evaluating brand importance: A use case from the fashion industry

Abstract

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.

Evaluating and improving social awareness of energy communities through semantic network analysis of online news

“…Our intuition is also supported by past studies showing that news can be a good predictor of social, economic, and political phenomena [53][54][55]. For example, Park et al [56] tested the role of news discourse in forecasting tourist arrivals in Hong Kong and confirmed that including news data significantly improves forecasting performance.…”

“…This composite indicator includes the assessment of three dimensions: (i) prevalence, a proxy for the media coverage of a specific topic, usually identified by a set of keywords that we call energy communities-related keywords (ECKs) in this work; (ii) diversity, i.e., the heterogeneity and uniqueness of the words associated with those of interest; and (iii) connectivity, i.e., the ability of ECKs to link different concepts, words, or discourse topics. This measure of semantic importance is recent but not entirely new since it has already been conceptualized, used, and validated in previous studies [53,54,63]. However, to the best of our knowledge, this is the first time it has been applied to the energy-community field of research.…”

Performance of Sentiment Classification on Tweets of Clothing Brands

“…Fronzetti Colladon et al [5] applied Semantic Brand Score to visualize 206,000 tweets for the mentions of the fashion brands Fendi, Gucci and Prada. They determined Gucci dominated the discourse, with highest values of prevalence, variety, and connectivity.…”