Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250500073-14.doi: 10.11896/jsjkx.250500073

• Artificial Intelligence • Previous Articles     Next Articles

Study on Financial Text Sentiment Analysis Method Based on Large Language Models with Market Feedback Supervision

ZHANG Yongyu1,2, GUO Chenjuan1, FEI Xueqin3, LI Feng4   

  1. 1 School of Data Science & Engineering,East China Normal University, Shanghai 200062,China
    2 Hundsun Technologies Inc.,Hangzhou 310052,China
    3 Zhejiang X Testing and Certification Technology Co., Ltd., Hangzhou 311400,China
    4 E-Capital Transfer Co., Ltd.,Shanghai 200131,China
  • Online:2026-06-16 Published:2026-06-12
  • About author:ZHANG Yongyu,born in 1979,master,senior engineer,is a member of CCF(No.R9350M).Hismain research interests include financial time series prediction and multimodal tasks.
    GUO Chenjuan,born in 1982,Ph.D,professor,Ph.D supervisor.Her main research interests include data management and data analysis.

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Abstract: In the financial market,market sentiment has a profound impact on asset prices and volatility.Although large language models bring opportunities for financial text sentiment analysis,current research still has issues such as difficulties in handling the professionalism and dynamics of financial texts and poor consistency with market reactions.This study constructs an innovative financial text sentiment analysis system.It integrates the advantages of multiple large language models,combines knowledge graph enhancement technology and chain-of-thought technology to optimize the hybrid language model framework.Moreover,it adopts a sliding analysis method that combines multiple time windows and dynamic weights,constructs a market index evaluation system,and develops an adaptive dynamic update algorithm to strengthen the market feedback supervision mechanism.Empirical analysis shows that this system performs excellently in the accuracy of sentiment analysis and has a high consistency with market reactions,significantly outperforming comparative models.This research provides new perspectives and tools for financial market research and investment decision-making,and holds great theoretical and practical significance in the financial field.

Key words: Financial text sentiment analysis, Large language models, Market feedback supervision, Multi-model fusion, Know-ledge graph

CLC Number: 

  • F224-39
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