BTC Sentiment Analysis on Social Media: A Comprehensive Study

Abstract

The influence of social media sentiment on the price of Bitcoin (BTC) has been a topic of interest for researchers and traders alike. This study aims to analyze the sentiment expressed in social media posts related to Bitcoin and investigate its correlation with the cryptocurrency’s market performance. We employ natural language processing (NLP) techniques and machine learning models to quantify sentiment and predict market trends.

Introduction

Bitcoin, as the first and most popular cryptocurrency, has experienced significant price volatility. Social media platforms have become a hub for discussions, news, and opinions that can potentially influence market sentiment. Understanding the dynamics between social media sentiment and BTC price movements is crucial for investors and regulators.

Methodology

Data Collection

We collected data from various social media platforms including Twitter, Reddit, and BitcoinTalk. Tweets, posts, and comments were scraped using APIs provided by these platforms. The data was filtered to include only those posts that contained specific keywords related to Bitcoin.

Preprocessing

Text data was cleaned by removing noise such as URLs, special characters, and stop words. Tokenization, stemming, and lemmatization were performed to standardize the text.

Sentiment Analysis

Sentiment analysis was conducted using both rule-based and machine learning approaches. For rule-based analysis, we utilized predefined dictionaries of positive and negative words. For machine learning, we trained a supervised model using a labeled dataset of social media posts with known sentiment.

Feature Engineering

Features extracted from the text data included the frequency of positive and negative words, the overall sentiment score, and the volume of posts per day.

Model Training

We employed a time series analysis model to predict BTC prices based on sentiment scores. Features from the sentiment analysis were used as input variables.

Results

Our analysis revealed a moderate correlation between negative sentiment and a decrease in BTC price. Conversely, positive sentiment was associated with price increases, although the correlation was not as strong.

Predictive Model Performance

The predictive model achieved an accuracy of 70% in forecasting short-term price movements based on sentiment analysis. However, the model’s performance degraded over longer prediction horizons.

Discussion

The findings suggest that while social media sentiment can provide insights into market sentiment, its predictive power is limited. The influence of other factors such as economic indicators and regulatory news cannot be overlooked.

Conclusion

This study contributes to the understanding of the relationship between social media sentiment and BTC price movements. Future research could explore the integration of sentiment analysis with other market data for more robust predictions.

References

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