Thematic Analysis of Big Data in Financial Institutions Using NLP Techniques with a Cloud Computing Perspective: A Systematic Literature Review

Sharma, RK; Bharathy, G; Karimi, F; Mishra, AV; Prasad, M

Thematic Analysis of Big Data in Financial Institutions Using NLP Techniques with a Cloud Computing Perspective: A Systematic Literature Review

Sharma, RK Bharathy, G

Karimi, F Mishra, AV Prasad, M

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Information (Switzerland), 2023, 14, (10)
Issue Date:: 2023-10-01

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Field	Value	Language
dc.contributor.author	Sharma, RK
dc.contributor.author	Bharathy, G https://orcid.org/0000-0001-8384-9509
dc.contributor.author	Karimi, F
dc.contributor.author	Mishra, AV
dc.contributor.author	Prasad, M
dc.date.accessioned	2024-01-15T05:54:07Z
dc.date.available	2024-01-15T05:54:07Z
dc.date.issued	2023-10-01
dc.identifier.citation	Information (Switzerland), 2023, 14, (10)
dc.identifier.issn	2078-2489
dc.identifier.issn	2078-2489
dc.identifier.uri	http://hdl.handle.net/10453/174482
dc.description.abstract	This literature review explores the existing work and practices in applying thematic analysis natural language processing techniques to financial data in cloud environments. This work aims to improve two of the five Vs of the big data system. We used the PRISMA approach (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for the review. We analyzed the research papers published over the last 10 years about the topic in question using a keyword-based search and bibliometric analysis. The systematic literature review was conducted in multiple phases, and filters were applied to exclude papers based on the title and abstract initially, then based on the methodology/conclusion, and, finally, after reading the full text. The remaining papers were then considered and are discussed here. We found that automated data discovery methods can be augmented by applying an NLP-based thematic analysis on the financial data in cloud environments. This can help identify the correct classification/categorization and measure data quality for a sentiment analysis.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Information (Switzerland)
dc.relation.isbasedon	10.3390/info14100577
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	46 Information and computing sciences
dc.title	Thematic Analysis of Big Data in Financial Institutions Using NLP Techniques with a Cloud Computing Perspective: A Systematic Literature Review
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2024-01-15T05:54:06Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	10

Abstract:

This literature review explores the existing work and practices in applying thematic analysis natural language processing techniques to financial data in cloud environments. This work aims to improve two of the five Vs of the big data system. We used the PRISMA approach (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for the review. We analyzed the research papers published over the last 10 years about the topic in question using a keyword-based search and bibliometric analysis. The systematic literature review was conducted in multiple phases, and filters were applied to exclude papers based on the title and abstract initially, then based on the methodology/conclusion, and, finally, after reading the full text. The remaining papers were then considered and are discussed here. We found that automated data discovery methods can be augmented by applying an NLP-based thematic analysis on the financial data in cloud environments. This can help identify the correct classification/categorization and measure data quality for a sentiment analysis.

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http://hdl.handle.net/10453/174482