The Financial Institution Text Data Mining and Value Analysis Model Based on Big Data and Natural Language Processing
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| Publié dans: | Journal of Organizational and End User Computing vol. 37, no. 1 (2025), p. 1-41 |
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| Autres auteurs: | , , |
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| Accès en ligne: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.4018/JOEUC.374213 |2 doi | |
| 035 | |a 3195634889 | ||
| 045 | 2 | |b d20250101 |b d20250331 | |
| 084 | |a 11187 |2 nlm | ||
| 100 | 1 | |a Yang, Juan |u Chongqing Technology and Business University, China | |
| 245 | 1 | |a The Financial Institution Text Data Mining and Value Analysis Model Based on Big Data and Natural Language Processing | |
| 260 | |b IGI Global |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Data processing | ||
| 653 | |a Time dependence | ||
| 653 | |a Data mining | ||
| 653 | |a Deep learning | ||
| 653 | |a Big Data | ||
| 653 | |a Trends | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Social networks | ||
| 653 | |a Value | ||
| 653 | |a Prices | ||
| 653 | |a Financial institutions | ||
| 653 | |a Sentiment analysis | ||
| 653 | |a Knowledge representation | ||
| 653 | |a Learning | ||
| 653 | |a Language attitudes | ||
| 653 | |a Value analysis | ||
| 653 | |a Time | ||
| 653 | |a Knowledge | ||
| 653 | |a Securities markets | ||
| 653 | |a Natural language processing | ||
| 653 | |a Volatility | ||
| 653 | |a Unstructured data | ||
| 653 | |a Financial analysis | ||
| 653 | |a Information retrieval | ||
| 653 | |a Real time | ||
| 700 | 1 | |a Bai, Yu |u Chongqing Technology and Business University, China | |
| 700 | 1 | |a Gong, Jie |u Chongqing Technology and Business University, China | |
| 700 | 1 | |a Han, Menghui |u Chongqing Technology and Business University, China | |
| 773 | 0 | |t Journal of Organizational and End User Computing |g vol. 37, no. 1 (2025), p. 1-41 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3195634889/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3195634889/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |