Gender preferences in cryptocurrency systems: Sentiment analysis and predictive modelling

Abstract

This study explored the role of gender preferences in cryptocurrency investments using sentiment analysis. X (Twitter) users’ gender (male/female) together with relevant sentiments (positive/negative) were extracted and investigated in this study. The Latent Dirichlet Allocation technique was utilised to model gender-related topics in an attempt to understand male and female users’ preferences to invest in cryptocurrency. The Apriori algorithm was employed to predict the highly associated investment terminologies with each gender. A predictive model was built to predict the type of digital currency preferred by X users. Using sentiment-based gender data, the results showed a high prediction accuracy (98.64%) of digital currency preferences. The study demonstrated that male users would most likely use Bitcoin, compared to female users who preferred Ethereum. This study further offers a novel mechanism to predict users’ preferences for cryptocurrency platforms using their sentiment features. It extends the knowledge of cryptocurrencies in the financial business profile by revealing how investors’ gender contributes to investment-related decisions.