Correlation Analysis of Social Media Sentiment on Short-Term Bitcoin Price Movements
DOI:
https://doi.org/10.54097/9a86js54Keywords:
Bitcoin; social media sentiment; short-term price; non-linear relationship; machine learning.Abstract
With the rapid development of digital finance, social media sentiment has gradually become a potential driver of cryptocurrency price fluctuations, but its impact mechanism on the short-term price of Bitcoin has not been fully explored. This article takes Reddit comment sentiment data from August to September 2021 and Bitcoin's short-term price (daily closing price) as a sample and explores the linear and nonlinear relationship between the two. Through correlation matrix, linear regression and generalised addability model (GAM) analysis, it is found that there is a weak positive linear correlation between emotion and price (Pearson coefficient = 0.3017, p<0.001), and there is a significant nonlinear effect (the intensity of emotional influence varies with the market state). The Granger causal test confirms that emotions have a one-way causal relationship with the price of Bitcoin. Further verification of the machine learning model, such as random forest, Long Short-Term Memory (LSTM) and the inclusion of emotional variables can improve the accuracy of price prediction. This study enriches the literature in the field of cryptocurrency market behaviour finance and provides an empirical basis for investors and regulators to monitor market sentiment risks.
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