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

Αποθηκεύτηκε σε:

Λεπτομέρειες βιβλιογραφικής εγγραφής
Εκδόθηκε σε:	International Journal of Advanced Computer Science and Applications vol. 16, no. 6 (2025)
Κύριος συγγραφέας:	PDF
Έκδοση:	Science and Information (SAI) Organization Limited
Θέματα:	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
Διαθέσιμο Online:	Citation/Abstract Full Text - PDF
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Περιγραφή
Περίληψη:	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
Πηγή:	Advanced Technologies & Aerospace Database