Leveraging Large Language Models for Sentiment Analysis: Multi-Modal Analysis of Decentraland's MANA Token
A study integrates Discord community sentiment with multi-modal financial data to enhance cryptocurrency price prediction. Using BERT-based LLM and LSTM models, the multi-modal approach significantly outperforms price-only baseline.
[2605.20192] Leveraging Large Language Models for Sentiment Analysis: Multi-Modal Analysis of Decentraland's MANA Token
[Submitted on 4 Apr 2026]
Title:Leveraging Large Language Models for Sentiment Analysis: Multi-Modal Analysis of Decentraland's MANA Token
View a PDF of the paper titled Leveraging Large Language Models for Sentiment Analysis: Multi-Modal Analysis of Decentraland's MANA Token, by Xintong Wu and 5 other authors
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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 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.
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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)
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