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Review

Text Mining for Big Data Analysis in Financial Sector: A Literature Review

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Faculty of Economics & Business, University of Zagreb, 10000 Zagreb, Croatia
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Atomic Intelligence, 10000 Zagreb, Croatia
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Faculty of Humanities and Social Sciences, Information and Communication Sciences, University of Zagreb, 10000 Zagreb, Croatia
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School of Economics and Business, University of Sarajevo, 71000 Sarajevo, Bosna i Hercegovina
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Author to whom correspondence should be addressed.
Sustainability 2019, 11(5), 1277; https://doi.org/10.3390/su11051277
Received: 15 January 2019 / Revised: 20 February 2019 / Accepted: 21 February 2019 / Published: 28 February 2019
Big data technologies have a strong impact on different industries, starting from the last decade, which continues nowadays, with the tendency to become omnipresent. The financial sector, as most of the other sectors, concentrated their operating activities mostly on structured data investigation. However, with the support of big data technologies, information stored in diverse sources of semi-structured and unstructured data could be harvested. Recent research and practice indicate that such information can be interesting for the decision-making process. Questions about how and to what extent research on data mining in the financial sector has developed and which tools are used for these purposes remains largely unexplored. This study aims to answer three research questions: (i) What is the intellectual core of the field? (ii) Which techniques are used in the financial sector for textual mining, especially in the era of the Internet, big data, and social media? (iii) Which data sources are the most often used for text mining in the financial sector, and for which purposes? In order to answer these questions, a qualitative analysis of literature is carried out using a systematic literature review, citation and co-citation analysis. View Full-Text
Keywords: big data; text mining; financial sector; data science; language big data; text mining; financial sector; data science; language
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MDPI and ACS Style

Pejić Bach, M.; Krstić, Ž.; Seljan, S.; Turulja, L. Text Mining for Big Data Analysis in Financial Sector: A Literature Review. Sustainability 2019, 11, 1277. https://doi.org/10.3390/su11051277

AMA Style

Pejić Bach M, Krstić Ž, Seljan S, Turulja L. Text Mining for Big Data Analysis in Financial Sector: A Literature Review. Sustainability. 2019; 11(5):1277. https://doi.org/10.3390/su11051277

Chicago/Turabian Style

Pejić Bach, Mirjana, Živko Krstić, Sanja Seljan, and Lejla Turulja. 2019. "Text Mining for Big Data Analysis in Financial Sector: A Literature Review" Sustainability 11, no. 5: 1277. https://doi.org/10.3390/su11051277

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