BTC Sentiment Statistics: Analyzing Public Perception of Bitcoin through Social Media Data

Abstract
The cryptocurrency market is heavily influenced by public sentiment. This paper investigates the role of social media in shaping the perception of Bitcoin (BTC) among the general public. We introduce BTC Sentiment Statistics, a novel framework that leverages natural language processing (NLP) and machine learning (ML) to analyze social media data and gauge the sentiment towards Bitcoin.

Introduction
Bitcoin, as the pioneer of cryptocurrencies, has experienced significant fluctuations in its value over the years. Public sentiment plays a crucial role in these fluctuations. Social media platforms serve as a hub for discussions and opinions on Bitcoin, making them a valuable source for sentiment analysis.

Methodology
Data Collection
We collected tweets and Reddit posts related to Bitcoin using the Twitter API and Reddit API. Our dataset spans from January 2018 to December 2022.

Preprocessing
The data was preprocessed by removing noise such as URLs, mentions, and hashtags. We also performed tokenization and stopword removal to prepare the data for sentiment analysis.

Sentiment Analysis
We employed a combination of NLP techniques including sentiment lexicons (e.g., AFINN, VADER) and ML models (e.g., LSTM, BERT) to classify the sentiment of each post as positive, negative, or neutral.

Sentiment Statistics
We calculated various statistics such as the average sentiment score, the proportion of positive/negative/neutral posts, and the sentiment distribution over time.

Results
Our analysis revealed that public sentiment towards Bitcoin is highly volatile and often correlates with market trends. We observed several peaks in negative sentiment during major market crashes, while positive sentiment surged during bull runs.

Correlation with Market Data
We found a strong correlation between the sentiment statistics and Bitcoin’s price movements. Positive sentiment was often followed by price increases, while negative sentiment preceded price drops.

Discussion
The results suggest that social media sentiment can be a valuable indicator for predicting market trends. However, it’s important to consider other factors such as market manipulation and the influence of news events.

Limitations
Our study is limited by the scope of the data sources and the accuracy of the sentiment analysis models. Future work could explore additional data sources and improve the sentiment analysis algorithms.

Conclusion
BTC Sentiment Statistics provides a comprehensive framework for analyzing public sentiment towards Bitcoin. By leveraging social media data, we can gain insights into market trends and make informed decisions. This study highlights the importance of sentiment analysis in the rapidly evolving cryptocurrency market.

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.
[3] Hamilton, J. D. (2008). Regime-switching models. Princeton University Press.

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*This article is for academic purposes only and does not constitute financial advice.*