Sentiment Extraction in Financial Texts from Decentralized Markets Using RoBERTa Language Model

Abstract

Sentiment analysis in financial texts plays a vital role in understanding market trends, especially in decentralized finance (DeFi) markets where information flow is rapid and largely unstructured. The emergence of large language models has significantly enhanced the ability to extract meaningful insights from unstructured data sources. However, existing sentiment analysis models often underperform on decentralized market texts due to their reliance on traditional financial datasets and their inability to capture domain-specific language, slang, and context. These models typically struggle with the informal, volatile, and jargon-rich nature of DeFi communications found in forums, social media, and blockchain-related discussions. To address these limitations, this paper propose Decentralized Finance Sentiment Extraction using a RoBERTa-based Neural Transformer (DeFiSENT). This framework incorporates a domain-adapted preprocessing pipeline tailored to DeFi language. It fine-tunes the RoBERTa model on curated, labeled datasets from decentralized platforms such as Reddit, Twitter, and DAO channels. Additionally, DeFiSENT integrates context-aware tokenization and class imbalance strategies to enhance performance in detecting sentiment across imbalanced and noisy text data. The proposed method can be deployed in applications such as real-time crypto sentiment dashboards, DeFi asset risk monitoring, and automated trading signal generation, offering a robust tool for both researchers and investors. Experimental evaluations demonstrate that DeFiSENT significantly outperforms baseline models in accuracy, F1-score, and generalization across unseen DeFi datasets. It effectively captures nuanced sentiments and outliers in dynamic financial discourse, providing timely and interpretable sentiment insights within decentralized finance ecosystems.