How are texts analyzed in blockchain research? A systematic literature review

This paper provides a systematic literature review of text analysis methodologies used in blockchain-related research to comprehend and synthesize existing studies across disciplines and define future research directions. We summarize the research scope, text data, and methodologies of 124 papers an...

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Bibliographic Details
Published in:Financial innovation (Heidelberg) 2024-12, Vol.10 (1), p.60-35, Article 60
Main Authors: Zhuo, Xian, Irresberger, Felix, Bostandzic, Denefa
Format: Article
Language:English
Subjects:
Blockchain
Economics
Economics and Finance
Literature reviews
Machine learning algorithm
Macroeconomics/Monetary Economics//Financial Economics
Political Economy/Economic Systems
Review
Sentiment analysis
Systematic literature review
Systematic review
Text analysis
Topic modeling
User generated content
Citations: Items that this one cites
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Summary:This paper provides a systematic literature review of text analysis methodologies used in blockchain-related research to comprehend and synthesize existing studies across disciplines and define future research directions. We summarize the research scope, text data, and methodologies of 124 papers and identify the two most common combinations of these dimensions: (1) papers that focus on specific cryptocurrencies tend to apply sentiment analysis to instant user-generated content or news articles to discover the correlations between public opinion and market behavior, and (2) studies that examine the broad concept of blockchain with text data from documents published by companies tend to apply topic modeling techniques to explore classifications and trends in blockchain development. We discover five major research topics in the academic literature: relationship discovery, cryptocurrency performance prediction, classification and trend, crime and regulation, and perception of blockchain. Based on these findings, we highlight three potential research directions for researchers to select topics and implement suitable methodologies for text analysis.
ISSN:2199-4730
2199-4730
DOI:10.1186/s40854-023-00501-6