Abstract
AbstractThis 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.
Publisher
Springer Science and Business Media LLC
Reference191 articles.
1. Abraham J, Higdon D, Nelson J, Ibarra J (2018) Cryptocurrency price prediction using tweet volumes and sentiment analysis. SMU Data Sci Rev 1(3):1
2. Akba F, Medeni IT, Guzel MS, Askerzade I (2021) Manipulator detection in cryptocurrency markets based on forecasting anomalies. IEEE Access 9:108819–108831
3. Alahi I, Islam M, Iqbal A, Bosu A (2019) Identifying the challenges of the blockchain community from Stackexchange topics and trends. 2019 IEEE 43rd Ann Comput Softw Appl Conf (COMPSAC) 1:123–128
4. Anamika A, Subramaniam S (2022) Do news headlines matter in the cryptocurrency market? Appl Econ 54(54):6322–6338
5. Aslam N, Rustam F, Lee E, Washington PB, Ashraf I (2022) Sentiment analysis and emotion detection on cryptocurrency related tweets using ensemble LSTM-GRU model. IEEE Access 10:39313–39324