Cryptocurrency Price Prediction using Twitter Sentiment Analysis

Paper: arXiv 2303.09397

Authors: Haritha GB, Sahana N. B

Abstract

The cryptocurrency ecosystem has been the centre of discussion on many social media platforms, following its noted volatility and varied opinions. Twitter is rapidly being utilised as a news source and a medium for bitcoin discussion. Our algorithm seeks to use historical prices and sentiment of tweets to forecast the price of Bitcoin. In this study, we develop an end-to-end model that can forecast the sentiment of a set of tweets (using a Bidirectional Encoder Representations from Transformers - based Neural Network Model) and forecast the price of Bitcoin (using Gated Recurrent Unit) using the predicted sentiment and other metrics like historical cryptocurrency price data, tweet volume, a user’s following, and whether or not a user is verified. The sentiment prediction gave a Mean Absolute Percentage Error of 9.45%, an average of real-time data, and test data. The mean absolute percent error for the price prediction was 3.6%.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 4.5/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper applies standard deep learning architectures (BERT and GRU) to a well-studied domain of crypto sentiment analysis. While it reports low prediction errors, the mathematical depth is minimal, and the empirical methodology lacks rigorous out-of-sample backtesting against trading strategies.

Research Flowchart

  flowchart TD
    A[Goal: Predict Bitcoin Price using Twitter Sentiment & Historical Data] --> B[Data Collection: Tweets & Historical Crypto Prices]
    B --> C[Sentiment Analysis: BERT Model]
    C --> D[Feature Metrics: Sentiment, Tweet Volume, User Verification, Followers]
    D --> E[Price Forecasting: GRU Neural Network]
    E --> F[Findings: Sentiment MAPE = 9.45%]
    E --> G[Findings: Price Prediction MAPE = 3.6%]