The CnOpenData Chinese Financial Text Corpus Database systematically collects financial text data from over 400 authoritative sources nationwide, with a cumulative data volume of 110 million entries. It covers core fields including titles, body content, and precise publication timestamps. Through scientific multi-source collection and standardized processing, this database establishes a comprehensive financial language resource repository spanning multiple platforms, time periods, and themes, providing panoramic data support for observing information flows and linguistic characteristics in China's capital markets.

Key Features:

• Data Uniqueness: Integrates unstructured texts scattered across various financial information platforms, transforming fragmented information into structured research material, thereby filling the gap for large-scale standardized corpora in the financial domain.
• Data Comprehensiveness: Covers continuous long-term time-series data to support longitudinal textual evolution analysis; balances macro policy interpretations with micro-level corporate dynamics in content dimensions.
• Data Reliability: Implements a quality filtering system through source weighting evaluation and cross-verification to ensure academic citation value.

Potential Applications:

• Academic Research: Supports cutting-edge topics such as financial text sentiment analysis, media attention measurement, and information disclosure effect studies; provides training foundations for computational linguistics, domain-specific dictionary construction, and semantic evolution modeling.
• Commercial Services: Empowers alternative data factor development in quantitative investment strategies; enhances public opinion monitoring modules for corporate competitive intelligence systems; delivers intelligent semantic understanding for fintech products.
• Policy Optimization: Assists regulators in understanding market information dissemination patterns; establishes benchmark references for policy text effect evaluation; reveals systemic risk transmission pathways through large-scale semantic network analysis.

This database constructs a language observation infrastructure with both breadth and depth through systematic integration of publicly available financial text resources in China. Its standardized structure and multi-dimensional attributes provide a reliable data foundation for interdisciplinary research, demonstrating significant value in advancing innovative applications of text analysis technology in the financial sector.

Time Range

As of September 2025 (real-time updated)

Field Specifications

Sample Data

Data Update Frequency

Real-time updates