SBS BI Workshop

SBS 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 shows how to successfully integrate Text Mining with Social Network Analysis for business and research. It presents the Semantic Brand Score (SBS) and other powerful methods and tools for analyzing semantic networks, studying brand/semantic importance, and performing advanced NLP tasks. The workshop also describes the functionalities of the SBS Business Intelligence App (SBS BI), designed to produce a wide range of analytics and mine textual data. We discuss several case studies and show how these methods have been used, for example, to predict tourism trends, select advertising campaign testimonials, push the energy transition or make economic, financial, and political forecasts. SBS BI 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., terms used in media communication that impact consumers’ feeling about the state of the economy). Combining text analysis with network science can change how we make decisions and manage organizations in the era of big data.

Activities and Learning Objectives

Activities

• Presentations followed by activity learning and discussion of case studies.
• Hands-on tutorial where participants are engaged in individual exercises/application of the App and short group work activities facilitated by the organizers.

Learning Objectives

• Learn the basics of text mining and Natural Language Processing.
• Measure semantic/brand importance along its three dimensions (prevalence, diversity, and connectivity) and evaluate semantic/brand image.
• Analyze brand positioning and generate business intelligence reports.
• Explain how techniques such as text analysis and the use of brand mapping allow marketing managers to assess how brands are positioned in the minds of consumers or other stakeholders and whether these associations are positive, negative, or neutral.
• Discuss how business practitioners and scholars can use methods and tools of text mining and social network analysis – for example, to complement/replace traditional market sensing techniques, including consumer focus groups, surveys, and political polls.

Workshop topics include (but are not limited to)

• Use of SBS BI for calculating semantic importance and/or brand memorability. Study of brand prevalence, diversity, and connectivity.
• Analysis of the brand/semantic image.
• Network topic models.
• Keywords extraction, Named Entity Recognition, and construction of semantic networks.
• Use of advanced lexicons and measures of text mining (sentiment, readability, informativeness and complexity, emotions, uncertainty, etc.).
• Advanced Social Network Analysis metrics (e.g., distinctiveness centrality and rotating leadership).
• Measures of network and text similarity.

Requirements

Some basic knowledge of social network analysis and/or text mining is 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:

• obtaining the Twitter API keys (see these useful instructions);
• having a text editor like Notepad++ installed.

Course Offerings

Instructors

Andrea Fronzetti Colladon and Francesca Grippa.

Upcoming editions

• SUNBELT 2023. XLIII International Social Networks Conference.

Past editions (selection)

• SUNBELT 2022. XLII International Social Networks Conference.
• ACM Web Science 2021. Globalisation, Inclusion and the Web in the Context of COVID.
• Networks 2021. A joint Sunbelt and Netsci conference.

SBS BI workshops are regularly offered as part of the bachelor's degree program in Management and AI at Kozminski University (Poland) and 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 Business Management and Analytics course offered by the master's degree program in Mechanical Engineering at the University of Perugia (Italy).

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.

• WEB APP & DOCS    Here is a version of the SBS BI web app and its documentation.
• DEMO   Here is a demo of one of the dashboards produced by the SBS BI app.
• PYTHON SHORT TUTORIAL   Fronzetti Colladon, A. (2019). Calculating the Semantic Brand Score with Python. Towards Data Science.
• YOUTUBE VIDEOS   Some videos describing the Semantic Brand Score, its applications and the Web App are available HERE.

Main References

• MAIN PAPER   Fronzetti Colladon, A. (2018). The Semantic Brand Score. Journal of Business Research, 88, 150-160. https://doi.org/10.1016/j.jbusres.2018.03.026
• WEB APP   Fronzetti Colladon, A., & Grippa, F. (2020). Brand Intelligence Analytics. In A. Przegalinska, F. Grippa, & P. A. Gloor (Eds.), Digital Transformation of Collaboration (pp. 125-141). Springer Nature Switzerland. https://doi.org/10.1007/978-3-030-48993-9_10

More articles, case studies and scientific materials are available HERE.

Supported by

The European Commission, under the framework of Horizon 2020 Programme, through the project SWS HEATING “Development and Validation of an Innovative Solar Compact Selective-Water-Sorbent-Based Heating System Project”, G.A. No 764025.