Causality between Sentiment and Cryptocurrency Prices

ArXiv ID: 2306.05803 “View on arXiv”

Authors: Unknown

Abstract

This study investigates the relationship between narratives conveyed through microblogging platforms, namely Twitter, and the value of crypto assets. Our study provides a unique technique to build narratives about cryptocurrency by combining topic modelling of short texts with sentiment analysis. First, we used an unsupervised machine learning algorithm to discover the latent topics within the massive and noisy textual data from Twitter, and then we revealed 4-5 cryptocurrency-related narratives, including financial investment, technological advancement related to crypto, financial and political regulations, crypto assets, and media coverage. In a number of situations, we noticed a strong link between our narratives and crypto prices. Our work connects the most recent innovation in economics, Narrative Economics, to a new area of study that combines topic modelling and sentiment analysis to relate consumer behaviour to narratives.

Keywords: Topic Modelling, Sentiment Analysis, Narrative Economics, Unsupervised Machine Learning, Twitter Data, Cryptocurrencies

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Street Traders
  • Why: The math complexity is low to moderate, relying primarily on standard NLP techniques like GSDMM and TF-IDF without advanced statistical derivations. The empirical rigor is relatively high due to the use of a large real-world dataset (10M+ tweets), explicit data collection and preprocessing steps, and application to cryptocurrency price data, making it more implementation-heavy.
  flowchart TD
    A["Research Goal: Causality between<br>Sentiment & Cryptocurrency Prices"] --> B["Data Input:<br>Twitter Microblogging Data"]
    B --> C["Computational Process 1:<br>Topic Modelling<br>Unsupervised ML"]
    B --> D["Computational Process 2:<br>Sentiment Analysis"]
    C & D --> E{"Integration & Analysis"}
    E --> F["Key Findings & Outcomes"]
    F --> G["Identified 4-5 Narratives<br>e.g., Investment, Tech, Regulation"]
    F --> H["Strong Link Discovered<br>between Narratives & Prices"]
    F --> I["Contribution:<br>Linking Narrative Economics<br>to Consumer Behavior"]