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<p>The intricate and varied data structures of blockchain technology pose a distinct barrier for comprehension and analysis. Current analytical techniques frequently face challenges in comprehensively analyzing the complexities of textual, multimodal, and temporal data from blockchain. The constraint is mainly because traditional methods are not able to efficiently manage the complex and interrelated structure of blockchain data, leading to a lack of comprehensive analytics. The proposed research presents an innovative framework for blockchain analytics driven by NLP (natural language processing) to address this gap. The system includes four innovative strategies: Multimodal Analysis, Sentiment Assessment and Emotions Recognition, Spatiotemporal Analysis and Contextualizing, and Cross-Blockchain Analysis. The components are tailored to address particular facets of blockchain data, employing cutting-edge methods to enhance research. Multimodal analysis is centered on the advanced utilization of Multifunctional Transformer Models. These models efficiently handle the several data kinds commonly seen in blockchain transactions, including text, photos, and audio sets. The system utilizes LSTM-based models along with emotion lexicons to analyses user feelings in blockchain interactions across many scenarios, offering a comprehensive grasp of emotional nuances. Temporal Graph Neural Networks (T-GNNs) revolutionize Temporal Analysis and Contextualization. This research introduces an innovative method for using natural language processing in blockchain analytics to gain a comprehensive and detailed insight on blockchain data samples. The framework incorporates many innovative strategies to overcome the drawbacks of current analytical methodologies and establishes a new benchmark in blockchain data analysis, offering substantial progress in the industry.</p>

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Designing an Efficient Novel Model to Enhance NLP-Driven Blockchain Analytics by Integrating Multimodal, Sentiment, and Temporal Data

  • 期刊论文

  • 发表日期：

    2025年7月26日

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  • Pratik K. Agrawal,
  • Hitesh Gehani,
  • Palak Keshwani,
  • Shilpa Laddha,
  • Nitin Rakesh

摘要

The intricate and varied data structures of blockchain technology pose a distinct barrier for comprehension and analysis. Current analytical techniques frequently face challenges in comprehensively analyzing the complexities of textual, multimodal, and temporal data from blockchain. The constraint is mainly because traditional methods are not able to efficiently manage the complex and interrelated structure of blockchain data, leading to a lack of comprehensive analytics. The proposed research presents an innovative framework for blockchain analytics driven by NLP (natural language processing) to address this gap. The system includes four innovative strategies: Multimodal Analysis, Sentiment Assessment and Emotions Recognition, Spatiotemporal Analysis and Contextualizing, and Cross-Blockchain Analysis. The components are tailored to address particular facets of blockchain data, employing cutting-edge methods to enhance research. Multimodal analysis is centered on the advanced utilization of Multifunctional Transformer Models. These models efficiently handle the several data kinds commonly seen in blockchain transactions, including text, photos, and audio sets. The system utilizes LSTM-based models along with emotion lexicons to analyses user feelings in blockchain interactions across many scenarios, offering a comprehensive grasp of emotional nuances. Temporal Graph Neural Networks (T-GNNs) revolutionize Temporal Analysis and Contextualization. This research introduces an innovative method for using natural language processing in blockchain analytics to gain a comprehensive and detailed insight on blockchain data samples. The framework incorporates many innovative strategies to overcome the drawbacks of current analytical methodologies and establishes a new benchmark in blockchain data analysis, offering substantial progress in the industry.