[Editor:] Milković, Marin [Editor:] Seljan, Sanja [Editor:] Pejić Bach, Mirjana [Editor:] Peković, Sanja [Editor:] Perovic, Djurdjica [Title:] Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019 [ISSN:] 1849-7950 [Publisher:] IRENET - Society for Advancing Innovation and Research in Economy [Place:] Zagreb [Year:] 2019 [Pages:] 67-75
Textual data and analysis can derive new insights and bring valuable business insights. These insights can be further leveraged by making better future business decisions. Sources that are used for text analysis in financial industry vary from internal word documents, email to external sources like social media, websites or open data. The system described in this paper will utilize data from social media (Twitter) and tweets related to Italian banks, in Italian. This system is based on open source tools (R language) and topic extraction model was created to gather valuable information. This paper describes methods used for data ingestion, modelling, visualizations of results and insights.
visualization data science FinTech topic modelling LDA