BUSINESS INTELLIGENCE AND THE SEMANTIC BRAND SCORE APP
INSTRUCTOR: PROF. ANDREA FRONZETTI COLLADON
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 Business Intelligence App (SBS BI), 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 text 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.
BIBLIOMETRIC AND SCIENCE MAPPING ANALYSES
FOR RESEARCH SYNTHESIS THROUGH BIBLIOMETRIX R PACKAGE
INSTRUCTORS: PROF. CORRADO CUCCURULLO & PROF. MASSIMO ARIA
Academic publications are dramatically growing at a fast pace and it is increasingly unfeasible to keep track of all that is being published. Moreover, the emphasis on empirical contributions has resulted in a voluminous and fragmented research stream and contested ﬁeld. Nowadays, stand-alone literature reviews are extensively used in various fields for synthesizing findings from previous research, for using effectively the existing base of knowledge and enlarging its boundaries, for providing evidence-based guidelines to practice.
Scholars use various qualitative and quantitative approaches to make sense of earlier findings. Among them, bibliometric and science mapping analyses are powerful approaches to perform systematic, transparent, and reproducible reviews, especially with big volumes of data (big data).
This seminar has two main goals. The first is introducing all the different types of research synthesis. The second is presenting bibliometric and science mapping analyses for systematic literature reviews. During the seminar, the participants will perform a bibliometric and science mapping analysis.
NOOJ ENVIRONMENT FOR RULE-BASED NATURAL LANGUAGE PROCESSING
INSTRUCTORS: PROF. MARIO MONTELEONE
NooJ is a linguistic development environment software as well as a corpus processor constructed by
Max Silberztein (ELLIAD, Université de Franche-Comté, France, http://elliadd.univ-fcomte.fr/fiches/silberzteinmax). It firstly originated in investigations by Silberztein and the INTEX community of linguist users into the Lexicon-Grammar approach of Maurice Gross’ (LADL, Université Paris 7, France, https://www.wikiwand.com/en/Maurice_Gross), which states that no grammar rule can be developed independently from a strict delimitation of its domain of application.
NooJ has been used as a corpus processor by researchers in Linguistics, History, Psychology, Literature studies, Sentiment Analysis projects, Data Mining, and for processing musical notes and sentences.