BTC Sentiment Trend: Analyzing Public Perception of Bitcoin Using Social Media Data
Abstract
This paper investigates the sentiment trends of Bitcoin (BTC) using social media data. We explore the correlation between public sentiment and Bitcoin’s price movements, aiming to understand how social media influences the cryptocurrency market.
Introduction
Bitcoin, as the first and most well-known cryptocurrency, has seen significant price fluctuations over the years. Public sentiment plays a crucial role in these price movements. Social media platforms like Twitter, Reddit, and Telegram serve as primary channels for the dissemination of information and opinions about Bitcoin. Analyzing these sentiments can provide valuable insights into market trends and potential price movements.
Methodology
We collected data from various social media platforms using APIs and web scraping techniques. We focused on keywords related to Bitcoin, such as ‘Bitcoin,’ ‘BTC,’ ‘cryptocurrency,’ etc. The collected data was then processed using natural language processing (NLP) techniques to determine the sentiment of each post. We used machine learning algorithms like Naive Bayes and LSTM networks to classify the sentiment as positive, negative, or neutral.
Sentiment Analysis
We analyzed the sentiment trends over different periods, correlating them with Bitcoin’s price movements. We found that positive sentiment on social media often precedes price increases, while negative sentiment is associated with price drops. This suggests that social media sentiment can be a leading indicator of market trends.
Price Correlation
We used statistical methods like Pearson correlation and regression analysis to establish the relationship between sentiment scores and Bitcoin prices. The results show a moderate positive correlation between positive sentiment and price increases, and a negative correlation between negative sentiment and price drops.
Discussion
Our findings indicate that social media sentiment can influence Bitcoin’s price movements. However, this relationship is not deterministic, as other factors like market conditions, regulatory changes, and macroeconomic factors also play a role. It is crucial for investors to consider multiple factors while making investment decisions.
Conclusion
This study demonstrates the potential of using social media data for sentiment analysis in the cryptocurrency market. By monitoring social media trends, investors can gain insights into market sentiment and make informed decisions. Future research can explore the impact of sentiment on other cryptocurrencies and the development of more sophisticated sentiment analysis models.
References
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