BTC Sentiment Research: Analyzing Public Perception and Market Influence
Abstract
This paper explores the impact of public sentiment on Bitcoin (BTC) prices through a comprehensive analysis of social media data, news articles, and online forums. We employ machine learning techniques to classify sentiment and examine its correlation with market fluctuations.
Introduction
Bitcoin, as the leading cryptocurrency, has experienced significant price volatility since its inception. Understanding the factors driving these fluctuations is crucial for investors and regulators. Sentiment analysis offers a lens into the collective perceptions of the public, which can influence market behavior.
Data Collection
We collected data from various sources including Twitter, Reddit, and financial news websites. Our dataset spans from January 2017 to December 2023, covering periods of both bull and bear markets.
Methodology
Sentiment Classification
We utilized natural language processing (NLP) techniques to classify sentiments as positive, negative, or neutral. Our model was trained on a labeled dataset of 10,000 tweets and 5,000 Reddit posts.
Feature Engineering
We extracted features such as the frequency of specific keywords, the use of emojis, and the context of discussions to enhance sentiment classification accuracy.
Correlation Analysis
We employed Pearson’s correlation coefficient to measure the relationship between sentiment scores and Bitcoin’s price changes.
Results
Our analysis revealed a strong positive correlation between positive sentiment and Bitcoin price increases. Conversely, negative sentiment was associated with price declines. Notably, sentiment from financial news had a more significant impact on short-term price movements compared to social media.
Discussion
The findings suggest that public sentiment plays a pivotal role in shaping Bitcoin’s market dynamics. Investors should consider sentiment analysis as a valuable tool for making informed decisions.
Conclusion
Sentiment analysis provides valuable insights into market trends and can be integrated into trading strategies. Future research should explore the long-term effects of sentiment on investment returns and the role of different platforms in shaping public perception.
References
[1] Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8.
[2] Preis, T., Moat, H. S., Stanley, H. E., & Bishop, S. R. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3, 1684.
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*Note: This is a fictional academic paper for illustrative purposes only. The findings and conclusions are not based on actual research.*