HAVELSAN, Information and Communication Technologies, 06510 Ankara, Turkey.
Department of Computer Engineering, Ostim Technical University, 06370 Ankara, Turkey.
Sensors (Basel). 2022 Jun 11;22(12):4419. doi: 10.3390/s22124419.
Traditional sentiment analysis methods are based on text-, visual- or audio-processing using different machine learning and/or deep learning architecture, depending on the data type. This situation comes with technical processing diversity and cultural temperament effect on analysis of the results, which means the results can change according to the cultural diversities. This study integrates a blockchain layer with an LSTM architecture. This approach can be regarded as a machine learning application that enables the transfer of the metadata of the ledger to the learning database by establishing a cryptographic connection, which is created by adding the next sentiment with the same value to the ledger as a smart contract. Thus, a "Proof of Learning" consensus blockchain layer integrity framework, which constitutes the confirmation mechanism of the machine learning process and handles data management, is provided. The proposed method is applied to a Twitter dataset with the emotions of negative, neutral and positive. Previous sentiment analysis methods on the same data achieved accuracy rates of 14% in a specific culture and 63% in a the culture that has appealed to a wider audience in the past. This study puts forth a very promising improvement by increasing the accuracy to 92.85%.
传统的情感分析方法基于文本、视觉或音频处理，使用不同的机器学习和/或深度学习架构，具体取决于数据类型。这种情况存在技术处理多样性和文化气质对分析结果的影响，这意味着结果可能会根据文化多样性而发生变化。本研究将区块链层与 LSTM 架构集成在一起。这种方法可以被视为一种机器学习应用，通过建立加密连接将分类帐的元数据传输到学习数据库，该连接通过将具有相同值的下一个情感添加到分类帐中作为智能合约来创建。因此，提供了一个“学习证明”共识区块链层完整性框架，该框架构成了机器学习过程的确认机制，并处理数据管理。该方法应用于一个带有负面、中性和积极情绪的 Twitter 数据集。在同一数据上的先前情感分析方法在特定文化中的准确率为 14%，在过去吸引了更广泛受众的文化中的准确率为 63%。本研究通过将准确率提高到 92.85%，提出了一个非常有前途的改进方法。
