Talk title: Profiling Innovators: an Emotional Text Mining and Social Network Analysis Approach
Francesca Greco, Andrea Fronzetti Colladon
The identification of innovative staff members is a challenge for many business companies. This study looks at employees’ communication, presenting a new procedure to identify and profile innovators. The procedure combines Emotional Text Mining (ETM) and social network analysis. ETM is an unsupervised methodology, which allows the profiling of people based on their communication; it is a bottom-up semiotic approach used to classify unstructured data. It allows the understanding of people’s symbolizations, representations and sentiment, about one or more discourse topics (Greco & Polli, 2019). ETM is a fast and relatively simple procedure, which can be used to extract meaningful information from large text corpora. In order to profile innovators and describe their social behavior, we analyzed the intranet forum of a large Italian company and one-year interactions of about 11,100 employees. These interactions produced a large text corpus, consisting of over 1.8 million tokens.