Visualization of Big Data Text Analytics in Financial Industry: A Case Study of Topic Extraction for Italian Banks

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.

Keywords: visualization, data science, FinTech, topic modelling, LDA

JEL Classification: C55, C8

Suggested Citation: Suggested Citation

Krstić, Živko and Seljan, Sanja and Zoroja, Jovana, Visualization of Big Data Text Analytics in Financial Industry: A Case Study of Topic Extraction for Italian Banks (September 12, 2019). 2019 ENTRENOVA Conference Proceedings, Available at SSRN: https://ssrn.com/abstract=3490108 or http://dx.doi.org/10.2139/ssrn.3490108