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

Abstract

This paper explores the use of sentiment analysis on social media data to gauge public perception of Bitcoin (BTC). We analyze the sentiment data collected from various platforms and discuss its implications for market forecasting and investment decisions.

Introduction

Bitcoin, as the first and most well-known cryptocurrency, has experienced significant fluctuations in its value over the years. One of the factors influencing these fluctuations is the sentiment of the public towards Bitcoin. Sentiment analysis is a method used to determine the emotional tone behind a series of words, and it can be applied to social media data to understand public opinion.

Methodology

Data Collection

We collected data from various social media platforms such as Twitter, Reddit, and BitcoinTalk. The data was collected using APIs provided by these platforms. The data collection process involved gathering posts, comments, and reactions related to Bitcoin.

Data Preprocessing

The collected data was cleaned and preprocessed to remove noise such as irrelevant posts, spam, and non-English content. We also normalized the text data to ensure consistency.

Sentiment Analysis

We used natural language processing (NLP) techniques to analyze the sentiment of the preprocessed data. The sentiment was classified into three categories: positive, negative, and neutral. We employed machine learning algorithms such as Naive Bayes, Support Vector Machines (SVM), and deep learning models to perform the sentiment analysis.

Feature Engineering

We extracted features from the text data such as the frequency of certain keywords, the use of emoticons, and the context in which Bitcoin was mentioned. These features were used to train our sentiment analysis models.

Results

The sentiment analysis revealed that the public sentiment towards Bitcoin is highly volatile and often influenced by recent news and market events. We observed that positive sentiment was generally associated with price increases, while negative sentiment was linked to price drops.

Correlation with Market Data

We correlated the sentiment data with historical Bitcoin price data and found a moderate correlation between the two. This suggests that public sentiment may have some predictive power for future price movements.

Discussion

The results of our analysis highlight the importance of monitoring public sentiment when making investment decisions in Bitcoin. While sentiment analysis is not a foolproof method for predicting market movements, it can provide valuable insights into the overall mood of the market.

Limitations

One of the limitations of our study is the reliance on social media data, which may not be representative of the entire market. Additionally, the sentiment analysis models may not capture the full complexity of human emotions.

Future Work

Future research could explore the use of more advanced NLP techniques and incorporate data from a wider range of sources to improve the accuracy of sentiment analysis.

Conclusion

In conclusion, sentiment analysis of social media data provides a valuable tool for understanding public perception of Bitcoin. By analyzing this data, investors can gain insights into market sentiment and make more informed decisions.

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. (2011). Data-driven sentiment analysis of economics texts. Journal of Informetrics, 5(4), 595-606.

[3] Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. LREC, 10, 1320-1326.