BTC Sentiment Regression: Analyzing Market Sentiment through Machine Learning

Abstract
This paper presents a novel approach to predicting Bitcoin (BTC) market sentiment using regression models. By analyzing historical data and applying machine learning techniques, we aim to understand the correlation between sentiment and market movements.

Introduction
Bitcoin, as the leading cryptocurrency, has been subject to significant volatility. Understanding market sentiment is crucial for investors and traders to make informed decisions. Sentiment analysis has been widely used in traditional financial markets, and its application to cryptocurrencies is gaining traction.

Methodology
Data Collection
We collected data from various sources including social media platforms, news articles, and financial forums. The data was preprocessed to remove noise and normalize text.

Feature Engineering
We extracted features such as the frequency of positive and negative words, the volume of posts, and the sentiment scores from pre-trained models.

Model Selection
We experimented with different regression models including Linear Regression, Decision Trees, and Random Forest.

Model Training and Validation
The models were trained on a dataset split into training and validation sets. Cross-validation was used to ensure the model’s robustness.

Results
Our models showed a strong correlation between sentiment scores and BTC price movements. The Random Forest model outperformed others with an R^2 score of 0.82.

Discussion
The results indicate that sentiment analysis can be a valuable tool for predicting market trends in cryptocurrencies. However, the models are sensitive to the quality and quantity of data.

Conclusion
BTC Sentiment Regression is a promising approach for understanding market dynamics. Future work will focus on improving model accuracy and incorporating real-time data.

References
[1] Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science.
[2] Thelwall, M., Buckley, K., & Paltoglou, G. (2010). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology.
[3] Zhang, X., Fuehres, H., & Gloor, P. (2016). Predicting stock market indices through Twitter.

—

*Note: This is a hypothetical academic paper outline for illustrative purposes only.*