BTC Sentiment Regression: Analyzing Market Sentiment to Predict Bitcoin Prices
Abstract
This paper aims to explore the relationship between market sentiment and Bitcoin (BTC) prices using sentiment analysis and regression models. We hypothesize that by analyzing the sentiment expressed in social media and news articles, we can predict future price movements of Bitcoin.
Introduction
Bitcoin, as the first and most well-known cryptocurrency, has seen significant price volatility since its inception. Understanding the factors that influence these price movements is crucial for investors and traders. One such factor is market sentiment, which reflects the collective opinion of market participants.
Literature Review
Previous studies have shown that market sentiment can significantly impact financial markets. For instance, positive news or social media posts can lead to increased buying pressure, while negative sentiment can result in selling pressure. However, the application of sentiment analysis to cryptocurrency markets is relatively new and presents unique challenges due to the global and decentralized nature of these markets.
Methodology
Data Collection
We collected data from various sources, including Twitter, Reddit, and financial news websites. The data was filtered to include only posts and articles related to Bitcoin.
Sentiment Analysis
Using natural language processing (NLP) techniques, we analyzed the sentiment of the collected data. We employed machine learning algorithms such as Naive Bayes and Support Vector Machines (SVM) to classify the sentiment as positive, negative, or neutral.
Feature Engineering
We extracted features from the sentiment scores and combined them with other relevant factors such as trading volume, market capitalization, and historical price data.
Regression Model
We used linear regression to model the relationship between the sentiment scores and Bitcoin prices. The model was trained on historical data and validated using a separate test set.
Results
Our results indicate a strong correlation between market sentiment and Bitcoin prices. Positive sentiment was associated with price increases, while negative sentiment was linked to price decreases. The regression model achieved a high R-squared value, indicating a good fit.
Discussion
The findings suggest that sentiment analysis can be a valuable tool for predicting Bitcoin price movements. However, the model’s performance may be affected by other factors such as market manipulation and external economic events. Future work could explore the integration of additional data sources and more sophisticated machine learning models.
Conclusion
This study demonstrates the potential of sentiment analysis in predicting Bitcoin prices. While the model shows promise, further research is needed to refine the approach and account for the complex dynamics of cryptocurrency markets.
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 hypothetical academic article for illustrative purposes only.*