BTC Sentiment Data: Analyzing Public Sentiment towards Bitcoin through Social Media

Abstract

The rapid growth and volatility of cryptocurrencies have made them a subject of intense interest and speculation. Bitcoin, being the most prominent cryptocurrency, is particularly affected by public sentiment. This paper explores the use of sentiment analysis on social media data to gauge public sentiment towards Bitcoin (BTC). We aim to understand the correlation between sentiment trends and Bitcoin’s market performance.

Introduction

Bitcoin, launched in 2009, has become a significant financial asset, with its value often influenced by public perception and market sentiment. Sentiment analysis has emerged as a powerful tool to quantify public opinion from unstructured data such as social media posts. This study focuses on collecting and analyzing social media data to determine the sentiment towards Bitcoin.

Methodology

Data Collection

We utilized APIs from major social media platforms such as Twitter, Reddit, and Bitcoin forums to collect a dataset of public posts related to Bitcoin. The data was collected over a period of one year, from January to December 2023.

Preprocessing

The collected data underwent rigorous preprocessing, including:
– Removal of irrelevant content
– Tokenization of text
– Lemmatization to reduce words to their base or root form
– Removal of stop words and punctuation

Sentiment Analysis

We employed machine learning models, specifically a Long Short-Term Memory (LSTM) network, to classify the sentiment of each post as positive, negative, or neutral. The model was trained on a labeled dataset and validated using a separate test set.

Results

The sentiment analysis revealed that public sentiment towards Bitcoin fluctuated significantly over the study period. Positive sentiment was highest during periods of market growth, while negative sentiment peaked during market downturns.

Correlation with Market Performance

A correlation analysis was conducted between the sentiment scores and Bitcoin’s price movements. A moderate positive correlation was observed, indicating that positive sentiment on social media tends to precede market upswings, and vice versa.

Discussion

The findings suggest that social media sentiment can be a valuable indicator of market sentiment and potentially influence Bitcoin’s price movements. However, the relationship is not deterministic, and other factors such as market conditions and regulatory changes also play a significant role.

Conclusion

This study demonstrates the potential of sentiment analysis in understanding market dynamics for cryptocurrencies like Bitcoin. While social media sentiment shows a correlation with market performance, it is crucial to consider it as one of many factors influencing Bitcoin’s value. Future research could explore the impact of different social media platforms individually and the role of influential users in shaping sentiment.

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.