BTCsentimentpeak: Analyzing Bitcoin Sentiment Peaks in Social Media Data
Abstract
The cryptocurrency market is heavily influenced by investor sentiment. This paper introduces BTCsentimentpeak, a novel framework for identifying and analyzing sentiment peaks in Bitcoin-related social media data. By leveraging natural language processing (NLP) and machine learning techniques, we aim to understand the relationship between sentiment peaks and market movements, providing insights for traders and investors.
Introduction
Bitcoin, as the leading cryptocurrency, has seen significant price volatility driven by various factors, including investor sentiment. Social media platforms serve as a rich source of sentiment data, reflecting the collective mood of the market. Identifying peaks in sentiment can help predict market trends and inform investment decisions.
Methodology
Data Collection
We collected Bitcoin-related tweets using Twitter’s API, focusing on keywords such as ‘Bitcoin,’ ‘BTC,’ and ‘cryptocurrency.’ Our dataset spans from January 2018 to December 2023, capturing various market cycles.
Sentiment Analysis
Using NLP techniques, we performed sentiment analysis on the collected tweets. We employed the VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis tool, which is specifically attuned to sentiments expressed in social media.
Sentiment Peak Detection
We defined a sentiment peak as a period where the average sentiment score significantly deviates from the mean, indicating heightened emotional reactions. We used a combination of statistical methods and machine learning models to detect these peaks.
Results
Sentiment Peaks and Market Correlation
Our analysis revealed a strong correlation between sentiment peaks and subsequent market movements. Peaks in positive sentiment often preceded market rallies, while negative peaks were associated with market downturns.
Case Studies
We conducted case studies on significant market events, such as the Bitcoin halving events in 2019 and 2023. These studies provided deeper insights into how sentiment peaks influenced market dynamics during these periods.
Discussion
The findings suggest that sentiment analysis of social media data can be a valuable tool for predicting market trends. However, the complexity of market factors also implies the need for a multifaceted approach that considers other economic indicators.
Conclusion
BTCsentimentpeak demonstrates the potential of leveraging social media sentiment analysis to inform investment strategies in the cryptocurrency market. Future work will focus on expanding the framework to include other cryptocurrencies and integrating real-time analysis capabilities.
References
[1] P. Pak and P. Paroubek. Twitter as a Corpus for Sentiment Analysis and Opinion Mining. LREC, 2010.
[2] H. Liu. Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 2012.
[3] S. M. Mohammad, et al. From Freq to Feel: Word Frequencies vs. Sentiment Analysis. ACL, 2013.