Semantic Brand Score BI - Workshop

Leveraging the power of big data represents an opportunity for researchers and managers to reveal patterns and trends in social behaviors and consumer perceptions. This workshop presents the Semantic Brand Score (SBS), a methodology of assessment of brand importance that combines methods and tools of Text Mining and Social Network Analysis (Fronzetti Colladon, 2018). The workshop also describes the functionalities of the Semantic Brand Score BI App, which has been designed to assess brand/semantic importance, analyze brand image and mine textual data. Its analytical power extends beyond “brands”, comprising applications to study: commercial brands (e.g. Pepsi vs Coke); products (e.g. pasta vs pizza); personal brands (e.g. name and image of political candidates); set of words representing values (e.g. a company’s core values) or concepts related to societal trends (e.g. words used in media communication that impact consumers’ feeling about the state of the economy). The App generates a wide range of analytics that have been used, for example, to build predictive models to understand tourism trends, select advertising campaign testimonials, and make economic, financial and political forecasts. Gaining a deeper understanding of brand importance and image can change the way we make decisions and manage organizations in the era of big data.

 Activities and Learning Objectives 
Activities:
Learning Objectives:

 Requirements 
Some basic knowledge of social network analysis and/or text mining could be a plus if participants plan to use this for academic purposes. However, previous knowledge of these methods is not required.

There are no strict software requirements as SBS BI is a web-based application. However, we suggest:

 Course Offerings 
Upcoming editions: Instructors: Andrea Fronzetti Colladon and Francesca Grippa.

SBS BI workshops are regularly offered as part of three master’s programs in Data Science, Business Management and Economic Intelligence, all held at the University of Rome Tor Vergata (Italy). The workshop is also part of the course of Business Management and Analytics offered at the master’s program in Mechanical Engineering of the University of Perugia (Italy). Previous editions include: 2020 Management and Artificial Intelligence Program of Kozminski University (Poland).

Please directly apply through the conference or university websites.

 Extra Materials 
Course materials will be provided to the attendees, during the course. Additional materials are linked in the following.

 Main References  Please Cite as