BTC Sentiment Study: Analyzing Public Opinion on Bitcoin Using Natural Language Processing (NLP)

Abstract

This study aims to investigate the sentiment towards Bitcoin (BTC) by analyzing public discourse on social media platforms and financial forums. By employing Natural Language Processing (NLP) techniques, we can quantify the emotional tone behind the conversations and gain insights into market sentiment, which could potentially influence the price of Bitcoin.

Introduction

Bitcoin, as the first and most well-known cryptocurrency, has attracted significant attention from investors and the general public alike. The sentiment of the public can significantly impact the price of Bitcoin, as it reflects the collective perception of market participants. This study focuses on analyzing the sentiment of Bitcoin-related discussions to understand its potential influence on the cryptocurrency’s price.

Methodology

Data Collection

We collected data from various sources, including Twitter, Reddit, and financial news websites. Tweets and Reddit posts were collected using the Twitter API and Reddit API, while news articles were scraped using web scraping techniques.

Data Preprocessing

The collected data was cleaned to remove noise such as special characters, URLs, and non-relevant information. Tokenization, stemming, and stop-word removal were performed to prepare the text data for sentiment analysis.

Sentiment Analysis

We utilized NLP techniques to analyze the sentiment of the text data. Specifically, we employed machine learning algorithms such as Naive Bayes, Support Vector Machines (SVM), and deep learning models like LSTM (Long Short-Term Memory) networks.

Model Training and Evaluation

The models were trained on a labeled dataset where the sentiment of each piece of text was manually tagged as positive, negative, or neutral. The performance of the models was evaluated using accuracy, precision, recall, and F1-score.

Results

The sentiment analysis revealed that the majority of the discussions were neutral, with a slight inclination towards positive sentiment. However, during periods of significant price fluctuations, the negative sentiment increased noticeably.

Discussion

The study indicates that while positive sentiment is prevalent in the general discourse, negative sentiment spikes during market downturns. This suggests that market sentiment is sensitive to price movements and can potentially influence the price of Bitcoin.

Conclusion

This study provides valuable insights into the relationship between public sentiment and the price of Bitcoin. By understanding the sentiment dynamics, investors and traders can make more informed decisions. Future research could explore the impact of sentiment on other cryptocurrencies and the integration of sentiment analysis in trading algorithms.

References

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[2] Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167.

[3] Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780.