BTC Sentiment Analysis Using Deep Learning: A Comprehensive Study

Abstract

This paper explores the application of deep learning techniques in analyzing Bitcoin (BTC) sentiment from social media and news sources. Sentiment analysis is a crucial aspect of understanding market dynamics, particularly in the volatile cryptocurrency market. We present a comprehensive study that leverages deep learning models to predict BTC price movements based on sentiment extracted from textual data.

Introduction

Bitcoin, as one of the most popular cryptocurrencies, has attracted significant attention from investors and traders. The sentiment of the market can significantly influence the price of BTC. Traditional financial analysis tools often fall short in predicting market movements due to the high volatility and the influence of social media and news. This has led to the exploration of alternative methods such as sentiment analysis using deep learning.

Literature Review

Several studies have been conducted on sentiment analysis in financial markets. However, the application of deep learning in the cryptocurrency sector is relatively new. Early studies relied on simple machine learning algorithms, but recent advancements in deep learning have shown promising results in capturing complex patterns in textual data.

Methodology

Data Collection

We collected data from various sources including Twitter, Reddit, and financial news websites. The dataset comprises tweets, forum posts, and news articles related to Bitcoin.

Preprocessing

The textual data was preprocessed to remove noise such as special characters, stop words, and irrelevant information. Tokenization and lemmatization were performed to standardize the text.

Model Selection

We experimented with several deep learning architectures including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks.

Training and Validation

The models were trained on a GPU-enabled environment using a large dataset split into training, validation, and test sets. We used cross-validation to ensure the robustness of our models.

Results

Our experiments showed that LSTM networks outperformed other models in terms of accuracy and F1 score. The sentiment analysis model was able to predict BTC price movements with a high degree of accuracy.

Discussion

The results indicate that deep learning can effectively be used to analyze sentiment in the cryptocurrency market. However, the model’s performance is highly dependent on the quality and quantity of the data. Future work could explore the integration of other data sources such as trading volume and price history to improve prediction accuracy.

Conclusion

This study demonstrates the potential of deep learning in sentiment analysis for Bitcoin. The integration of sentiment analysis with traditional financial analysis tools could provide a more comprehensive view of market dynamics.

References

[1] Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).

[2] Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780.

[3] Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems – Volume 2 (NIPS’13).

[4] Pennington, J., Socher, R., & Manning, C. (2014). Glove: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).