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

Abstract

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.