Financial Sentiment Analysis Using Embedding Vectors + Lightweight Models: A Practical Solution with 90% Cost Reduction

This project proposes an efficient financial text sentiment analysis framework: generating 256-dimensional semantic vectors via OpenAI text-embedding-3-small, combined with classification using a PyTorch logistic regression model. While maintaining an accuracy rate of over 94%, this solution reduces inference costs by 90%, addressing the high inference cost and long response latency of traditional large models, and providing a feasible engineering solution for real-time financial sentiment analysis.

Financial text sentiment analysis has unique complexities: it is filled with professional terminology, financial indicators, and subtle semantics (e.g., "2.8x subscription in treasury bond auction" is positive, while "increase in accounts receivable turnover days" is negative). Traditional solutions that directly use large models like GPT for inference, although accurate, have high API call costs and long response latency, making them difficult to handle scenarios involving massive financial text processing.

The core innovation is separating semantic extraction and classification decision-making:

1. Semantic Extraction: Generate 256-dimensional vectors using OpenAI text-embedding-3-small, which has low cost for a single forward pass and contains rich semantics;
2. Classification Decision-Making: A lightweight PyTorch logistic regression model (linear layer + Sigmoid), trained with Adam optimizer + binary cross-entropy for 200 epochs until convergence. Transfer Learning Strategy: First train on a dataset of 10,000 general tweets, then apply zero-shot to financial texts. Due to the strong generalization of the embedding model, the decision boundary is effectively transferred.

Success Cases: Can correctly classify complex financial texts (e.g., positive cases involving temporary working capital pressure but reduced debt + share repurchase; negative cases involving slowing growth + worsening cash flow). Error Analysis: 4 misclassified samples are concentrated in professional financial mechanisms (e.g., "deepening yield curve inversion" was predicted as positive but is actually negative due to semantic contradiction in "deepening"), exposing the insufficiency of general embeddings in handling subtle differences in professional finance. Improvement requires domain-specific embeddings or more financial samples.

Cost Advantages: The cost of embedding generation is far lower than that of large model APIs; the lightweight model can be trained on CPU without GPU, and inference latency is low (single forward pass). Tech Stack: Data processing (Pandas/NumPy/NLTK), embedding generation (OpenAI API), model training (PyTorch logistic regression), evaluation (financial test set). The code structure is clear, including modules such as data pipeline and model definition.

Application Scenarios: Real-time market sentiment monitoring, portfolio risk management, quantitative trading strategies, regulatory compliance review. Summary: The hybrid architecture of "large models for representation + small models for decision-making" significantly reduces costs while maintaining capabilities, which is a practical path for LLMOps. Expansion Directions: Introduce domain-specific embeddings (e.g., FinBERT), explore shallow neural networks, and build online learning mechanisms.