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Deep-Learning-Based Financial Message Sentiment Classification in Business Management.

Chen Shao1, Xiaochen Chen2

  • 1School of Intelligent Engineering, Shandong Management University, Jinan 250357, China.

Computational Intelligence and Neuroscience
|July 28, 2022
PubMed
Summary
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This study introduces a deep learning method for financial sentiment analysis, improving accuracy by using domain adaptation to overcome limited data. The approach effectively transfers learning to new financial datasets, enhancing business insights.

Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Financial Technology

Background:

  • Financial text sentiment classification faces challenges due to insufficient labeled data.
  • Domain adaptation is crucial for improving model performance in specialized text domains.
  • Effective sentiment analysis provides valuable insights for business management and investment strategies.

Purpose of the Study:

  • To propose a novel deep-learning-based method for financial text sentiment classification.
  • To address the issue of limited labeled samples in the financial domain using domain adaptation.
  • To enhance the accuracy and efficiency of sentiment analysis in financial contexts.

Main Methods:

  • A seq2seq model is employed for abstractive summarization of financial messages to reduce noise and accelerate processing.

Related Experiment Videos

  • A bidirectional Long Short-Term Memory (LSTM) network is utilized for comprehensive context-aware sentiment classification.
  • Domain adaptation techniques are applied to transfer knowledge from a general dataset (Amazon) to a specific financial dataset (StockTwits).

    • Main Results:

    • The proposed method demonstrates effective knowledge transfer from a reduced Amazon dataset to the StockTwits financial text dataset.
    • Recognition rates improved by 0.5% compared to parameter-frozen methods and 6.8% compared to the SDA-based method.
    • When trained directly on target domain data, the method outperformed SVM by 8.3% and LSTM by 4.5%.

    Conclusions:

    • The proposed deep learning approach with domain adaptation significantly improves financial text sentiment classification accuracy.
    • The method offers a practical solution for analyzing financial sentiment with limited labeled data.
    • This technique provides a valuable reference for business management and financial market analysis.