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

Konferenzbeitrag

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: In: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019 ; Year: 2019 ; Pages: 67-75 ; Zagreb: IRENET - Society for Advancing Innovation and Research in Economy

Classification and Topics

• Classification: Wirtschaft ; Large Data Sets: Modeling and Analysis
• Subject: visualization ; data science ; FinTech ; topic modelling ; LDA

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Krstić, Živko ; Seljan, Sanja ; Zoroja, Jovana
• Event: Veröffentlichung
• (who): IRENET - Society for Advancing Innovation and Research in Economy
• (where): Zagreb
• (when): 2019

Further information

• Handle: http://hdl.handle.net/10419/207665
• Last update: 10.03.2025, 11:41 AM CET