Leveraging Large Language Models for Sentiment Analysis: Multi-Modal Analysis of Decentraland's MANA Token
arXiv:2605.20192v1 Announce Type: new Abstract: Decentraland, a decentralized virtual reality platform operating within the expanding Metaverse ecosystem, utilizes its native MANA token to facilitate virtual asset transactions and governance. This study investigates the integration of Discord community sentiment with multi-modal financial data to enhance cryptocurrency price prediction within virtual world economies. We address: (1) identifying sentiment patterns within Decentraland's Discord community, and (2) evaluating the impact of multi-modal features on token return forecasting. Using a
The proliferation of Large Language Models (LLMs) and the increasing maturity of virtual world economies like Decentraland allow for novel applications in financial prediction, addressing a current gap in crypto market analysis.
This study demonstrates how advanced AI, specifically LLMs, can integrate disparate data types (social sentiment, financial data) to improve predictive accuracy in volatile digital asset markets, offering a template for broader market intelligence.
The ability to leverage AI for predicting token returns based on multi-modal data, rather than purely financial indicators, introduces a new dimension to cryptocurrency analysis and investment strategies.
- · AI-powered financial analytics platforms
- · Decentralized virtual economies
- · Quantitative traders
- · Data scientists skilled in multi-modal AI
- · Traditional qualitative market analysts
- · Investors relying solely on technical analysis
Improved predictability for virtual asset prices in platforms like Decentraland based on social sentiment and financial data.
Increased adoption of AI and LLMs for real-time market sentiment analysis and automated trading strategies in broader crypto markets.
Potential for new financial instruments and investment vehicles directly linked to AI-derived sentiment within virtual economies.
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