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Application Analysis and Research of Text Model Based on Improved CNN-LSTM in the Financial Field

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Publicado en:International Journal of Advanced Computer Science and Applications vol. 16, no. 6 (2025)
Autor principal: PDF
Publicado:
Science and Information (SAI) Organization Limited
Materias:
News
Situational awareness
Sentiment analysis
Prediction models
Texts
Public opinion
Text categorization
Computer science
Investments
Trends
Securities markets
Text analysis
Neural networks
Social networks
Classification
Information processing
Natural language processing
Emotions
Semantics
Investor behavior
Acceso en línea:Citation/Abstract
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Resumen:With the continuous development of information technology, public opinion analysis based on open-source texts and financial situation awareness has become a research hotspot. This study focuses on financial news and commentary information. First, a topic crawler classification model combining the advantages of CNN and LSTM is proposed to improve the topic recognition ability of financial news texts, and a CNN-LSTM-AM stock price fluctuation prediction model is proposed. This model performs sentiment analysis through BiLSTM, integrates multiple emotional factors and market historical data, and demonstrates superior predictive performance compared to traditional models in multiple experiments.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2025.0160675
Fuente:Advanced Technologies & Aerospace Database