Leveraging Large Language Models for Sentiment Analysis: Multi-Modal Analysis of Decentraland's MANA Token
9h ago
Quick Take
This study enhances cryptocurrency price prediction by integrating Discord sentiment and multi-modal financial data for Decentraland's MANA token.
Key Points
- Utilizes BERT for sentiment analysis in Discord.
- Develops LSTM models for price prediction.
- Multi-modal model outperforms baseline in accuracy.
📖 Reader Mode
~2 min readAbstract: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 BERT-based large language model for sentiment analysis, we develop two LSTM architectures: a baseline incorporating historical prices and a multi-modal variant integrating sentiment scores, trading volume, and market capitalization. Results indicate predominantly neutral community sentiment with a positive skew. The multi-modal model significantly outperforms the price-only baseline in prediction accuracy. These findings demonstrate the predictive value of community-derived signals for virtual economy forecasting and establish a foundation for future research at the intersection of immersive virtual environments, natural language processing, and cryptocurrency market analysis.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.20192 [cs.CL] |
| (or arXiv:2605.20192v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.20192 arXiv-issued DOI via DataCite |
Submission history
From: Luyao Zhang [view email]
[v1]
Sat, 4 Apr 2026 04:04:13 UTC (1,144 KB)
— Originally published at arxiv.org
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