The Financial Institution Text Data Mining and Value Analysis Model Based on Big Data and Natural Language Processing

Abstract

Financial markets are inherently complex and influenced by a variety of factors, making it challenging to predict trends and detect key events. Traditional models often struggle to integrate both structured, or numerical, and unstructured, or textual, data; additionally, they fail to capture temporal dependencies or the dynamic relationships between financial entities. To address this, the multidimensional integrated model for financial text mining and value analysis (MI-FinText), was proposed. MI-FinText integrated multi-task learning, temporal graph convolutional networks and dynamic knowledge graph construction. MI-FinText simultaneously performed sentiment analysis, event detection, and value prediction by learning shared representations across tasks and modeling time-dependent relationships between financial events. MI-FinText continuously updated a dynamic knowledge graph to reflect the evolving financial landscape, enabling real-time insights.