Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/207665
Authors: 
Krstić, Živko
Seljan, Sanja
Zoroja, Jovana
Year of Publication: 
2019
Citation: 
[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 [Pages:] 67-75
Abstract: 
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.
Subjects: 
visualization
data science
FinTech
topic modelling
LDA
JEL: 
C55
C8
Document Type: 
Conference Paper

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