BTC Sentiment Trend: Analyzing Public Opinion on Bitcoin Using Machine Learning Techniques
Abstract
This paper explores the application of machine learning algorithms to analyze the sentiment trend of Bitcoin (BTC) on social media platforms. The goal is to understand how public opinion influences the price and market dynamics of Bitcoin.
Introduction
Bitcoin, as the first and most popular cryptocurrency, has seen significant growth and volatility in recent years. One of the factors influencing its price is the sentiment of the public, which can be gauged through social media platforms. Sentiment analysis, the process of computationally identifying and categorizing opinions expressed in text, is a crucial tool for understanding market trends.
Methodology
Data Collection
Data was collected from various social media platforms including Twitter, Reddit, and Bitcoin forums. Tweets, posts, and comments were scraped using Python libraries such as Tweepy and PRAW.
Preprocessing
The collected data underwent several preprocessing steps including tokenization, removal of stop words, and stemming. This was done to prepare the data for sentiment analysis.
Sentiment Analysis
We employed Natural Language Processing (NLP) techniques to analyze the sentiment of the collected data. Algorithms like Naive Bayes, Logistic Regression, and Deep Learning models were used to classify the sentiment as positive, negative, or neutral.
Trend Analysis
Sentiment scores were aggregated over time to identify trends. Time series analysis was performed to correlate sentiment trends with Bitcoin’s price movements.
Results
Our analysis revealed that there is a significant correlation between public sentiment and Bitcoin’s price. Periods of high positive sentiment often precede price increases, while negative sentiment is associated with price drops.
Discussion
The results suggest that social media sentiment can be a valuable indicator for predicting market trends. However, it’s important to note that sentiment analysis is not foolproof and should be used in conjunction with other market analysis tools.
Conclusion
This study provides evidence that sentiment analysis can be effectively used to track and predict Bitcoin’s market trends. Future work could involve refining the sentiment analysis model and incorporating more data sources for a more comprehensive analysis.
References
[1] Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies.
[2] Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval.
[3] Bollen, J., Mao, H., & Zeng, X. (2011). Twitter Mood Predicts the Stock Market. Journal of Computational Science.
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*Note: This is a hypothetical academic article and does not represent actual research.*