BTC Sentiment Analysis Using Natural Language Processing Techniques
Abstract
In the realm of cryptocurrencies, sentiment analysis plays a pivotal role in understanding market dynamics. This paper explores the application of natural language processing (NLP) techniques to analyze Bitcoin (BTC) sentiment from social media and news articles. We aim to develop a model that can predict market trends based on the sentiment derived from textual data.
Introduction
Bitcoin, as the leading cryptocurrency, has seen significant market fluctuations influenced by various factors, including investor sentiment. Sentiment analysis, a subset of NLP, focuses on identifying and extracting subjective information from source data. By analyzing textual data from social media platforms and news outlets, we can gauge the prevailing sentiment towards BTC, which can potentially inform investment decisions.
Literature Review
Previous studies have shown that social media sentiment correlates with market movements. For instance, a positive sentiment surge on Twitter often precedes a rise in Bitcoin’s value. However, the relationship is not always linear, and other factors such as market manipulation and economic indicators must be considered.
Methodology
Data Collection
We collected data from various sources including Twitter, Reddit, and financial news websites. The data was filtered to include only Bitcoin-related content.
Preprocessing
Text data was cleaned to remove noise such as special characters, URLs, and non-informative words. Tokenization and lemmatization were applied to standardize the text.
Feature Extraction
TF-IDF and word embeddings were used to convert text data into a numerical format suitable for machine learning models.
Model Development
Several machine learning algorithms were tested, including Naive Bayes, Logistic Regression, and SVM. Additionally, deep learning models like LSTM and BERT were explored for their ability to capture context and sequence dependencies in text.
Evaluation Metrics
Accuracy, Precision, Recall, and F1-Score were used to evaluate model performance.
Results
The LSTM model outperformed other algorithms with an F1-Score of 0.82, indicating its effectiveness in capturing sequential patterns in sentiment data.
Discussion
The results suggest that deep learning models are better suited for sentiment analysis in the context of cryptocurrencies due to their ability to understand context and relationships between words. However, the models are sensitive to the quality and volume of training data.
Conclusion
Our study demonstrates the potential of NLP in predicting BTC market trends through sentiment analysis. Future work will focus on integrating real-time data feeds and expanding the model to other cryptocurrencies.
References
[1] Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8.
[2] Thelwall, M., Buckley, K., & Paltoglou, G. (2010). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406-418.