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Bert Base Chinese Finetuning Financial News Sentiment V2
B
Bert Base Chinese Finetuning Financial News Sentiment V2
Developed by hw2942
A BERT-based Chinese financial news sentiment analysis model for determining the emotional tendency of financial news texts.
Downloads 410
Release Time : 6/14/2023
Model Overview
This model is based on the BERT architecture, specifically designed for sentiment analysis of Chinese financial news, capable of identifying positive, negative, or neutral sentiments in news texts.
Model Features
Specialized for Financial Domain
Optimized for financial news texts, accurately recognizing sentiment expressions unique to the financial field.
Based on BERT Architecture
Utilizes the bert-base-chinese pre-trained model with robust Chinese text comprehension capabilities.
Few-shot Fine-tuning
Fine-tuned with 2,000 training samples and 329 validation samples, suitable for few-shot learning scenarios in specific domains.
Model Capabilities
Financial News Sentiment Analysis
Chinese Text Classification
Financial Domain-specific Term Recognition
Use Cases
Financial Market Analysis
Stock Market News Sentiment Analysis
Analyze the sentiment tendency of stock market-related news to predict market sentiment changes.
Can identify positive or negative sentiments in news, such as 'Shanghai Composite Index closed at 3233.67 points, up 0.15%'.
Financial Policy Impact Assessment
Evaluate the sentiment tendency of financial policy news to analyze potential impacts of policies.
For example, analyzing policy texts like 'NDRC and seven other departments: Support eligible industry-education integration enterprises to list and finance'.
Macroeconomic Monitoring
Economic Data Release Analysis
Analyze the sentiment tendency of economic data release news.
For example, analyzing data texts like 'China's May new social financing and new RMB loans both declined year-on-year'.
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