Financial Text Sentiment Analysis Technology Based on Large Language Models

Financial Text Sentiment Analysis Technology Based on Large Language Models

Topic Description
Financial text sentiment analysis aims to extract market sentiment from unstructured texts such as news, financial reports, and social media to assist investment decisions or risk warnings. Traditional methods rely on dictionaries and simple machine learning models, but large language models (e.g., GPT, BERT) have significantly improved analysis accuracy through deep semantic understanding. This topic requires explaining its technical principles, data preprocessing, model optimization, and financial scenario applications.

Technical Principles

  1. Text Semantic Representation:

    • Traditional methods (e.g., TF-IDF) only count word frequency and cannot capture context (e.g., ambiguity in "Apple's stock price rose" vs. "Apples are delicious").
    • Large language models use Transformer architecture and generate word vectors through self-attention mechanisms, where the representation of each word is dynamically adjusted according to the context. For example, BERT's pre-training tasks (Masked Language Model) enable it to learn to predict masked words based on context, thereby understanding specific meanings of financial terms (e.g., "short selling" vs. "bear market").

  2. Sentiment Polarity Quantification:

    • Financial text sentiment is often categorized into three classes (positive/negative/neutral), but it is necessary to distinguish explicit sentiment (e.g., "earnings report exceeds expectations") and implicit sentiment (e.g., "The Fed hints at interest rate hikes" implies market tightening expectations).
    • Large language models are adapted to the financial domain through fine-tuning: training classification layers using annotated financial news data, enabling the model to learn the sentiment tendencies of professional vocabulary such as "reserve requirement cut" and "bad debt."

Implementation Steps

  1. Data Preprocessing and Financial Dictionary Enhancement:

    • Clean text: remove special characters, standardize terminology (e.g., unify "沪深300" as "HS300").
    • Expand domain dictionary: add financial-specific terms (e.g., "quantitative easing," "black swan") and abbreviations (e.g., "ETF") to avoid model misjudgment.
    • Example: The sentence "The company's IPO subscription is hot" requires recognizing "IPO" as a new stock issuance and "hot" as a positive signal.

  2. Domain-Adaptive Fine-Tuning:

    • Select a base model (e.g., FinBERT pre-trained in the financial domain) and further fine-tune it on annotated financial text data (e.g., Reuters news).
    • Fine-tuning methods:
      • Adjust the classification layer: map the model's final output to sentiment labels and optimize using a cross-entropy loss function.
      • Layered learning rate: slightly adjust low-level parameters (retain general language knowledge) and update high-level parameters more significantly (adapt to financial tasks).

  3. Sentiment Ambiguity Resolution:

    • Financial texts often contain sarcasm or conditional sentences (e.g., "If not for regulatory intervention, the bubble would burst"), requiring analysis combined with event logic.
    • Solutions:
      • Introduce external knowledge: connect knowledge graphs (e.g., corporate relationship networks) to judge the differential impact of "layoffs" on different companies.
      • Expand context window: use long-text models (e.g., Longformer) to analyze entire paragraphs, avoiding taking statements out of context.

Financial Scenario Applications

  1. Stock Price Volatility Warning:

    • Integrate multi-source texts (social media + financial reports) and calculate the correlation between sentiment scores and stock prices. For example, empirical studies on Tesla's Twitter sentiment and stock prices show that negative sentiment predicts declines 1-2 hours in advance.
    • Challenge: Market overreactions may decouple sentiment from stock prices, requiring the addition of volatility indicators to filter noise.

  2. Public Opinion Monitoring System:

    • Real-time news crawling, batch analysis of sentiment trends through models, and triggering risk control alerts (e.g., when short reports appear intensively).
    • Deployment optimization: use model distillation techniques to compress large models into lightweight models to meet low-latency requirements.

Challenges and Optimization Directions

  • Data bias: Financial texts contain predominantly negative news, requiring dataset balancing through oversampling or loss function weighting.
  • Dynamic evolution: New market terms frequently emerge (e.g., "metaverse concept stocks"), necessitating continuous incremental model training.
  • Interpretability: Use attention visualization to highlight keywords (e.g., highlight related words when the model judges "debt default" as negative) to enhance regulatory credibility.

By combining domain knowledge with the semantic understanding capabilities of large language models, financial sentiment analysis can evolve from shallow keyword matching to deep logical reasoning, becoming a core tool for intelligent investment advisory and risk control.