BTC Sentiment Analysis: Bearish Sentiment Detection in Cryptocurrency Markets

Abstract

The cryptocurrency market is highly volatile and sentiment plays a crucial role in its dynamics. Bearish sentiment, in particular, can have significant impacts on market prices. This paper aims to explore the detection of bearish sentiment in Bitcoin (BTC) discussions and its correlation with market trends. We utilize natural language processing (NLP) techniques and machine learning (ML) models to analyze textual data from various sources such as social media, forums, and news articles.

Introduction

Bitcoin, as the leading cryptocurrency, is subject to various market sentiments that influence its price. Bearish sentiment, characterized by negative outlooks and pessimistic predictions, can lead to a decline in prices. Detecting such sentiment early can provide valuable insights for traders and investors.

Data Collection

We collected data from multiple sources including Twitter, Reddit, and financial news websites. The dataset comprises tweets, forum posts, and articles spanning over a period of two years.

Methodology

1. Preprocessing

Text data was preprocessed to remove noise such as special characters, stop words, and non-informative words. Tokenization and stemming were also performed to standardize the text.

2. Feature Extraction

TF-IDF (Term Frequency-Inverse Document Frequency) and word embeddings were used to convert text data into a numerical format suitable for machine learning models.

3. Sentiment Analysis

We employed sentiment analysis models to classify the sentiment of each text piece into positive, negative, or neutral. Specifically, we focused on identifying bearish sentiment.

4. Machine Learning Models

Several ML models were tested, including Logistic Regression, Support Vector Machines (SVM), and Neural Networks, to classify bearish sentiment.

5. Evaluation

The models were evaluated using accuracy, precision, recall, and F1-score. A confusion matrix was also utilized to understand the performance in detail.

Results

Our analysis showed that the SVM model performed best in detecting bearish sentiment with an F1-score of 0.82. The results were then correlated with historical BTC price data to observe any patterns.

Discussion

The study revealed a strong correlation between bearish sentiment and BTC price drops. However, it’s important to note that sentiment is just one of many factors influencing cryptocurrency prices.

Conclusion

Bearish sentiment detection can provide valuable insights into market trends. Further research could explore real-time sentiment analysis and its integration with trading algorithms.

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] Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies.