Indexes derived from People's Daily articles using neural topic modeling. The dataset is hosted on Kaggle, but the author, organization, and temporal coverage are unknown. The specific methodology involves analyzing news text to quantify policy-related economic uncertainty.
Use Cases
- Modeling the impact of policy uncertainty on financial markets based on derived index values.
- Analyzing trends in economic policy discourse over time based on news article analysis.
- Correlating media-reported policy shifts with macroeconomic indicators based on the uncertainty index.
Strengths
- Derived from People's Daily, a major official newspaper in China.
- Uses neural topic modeling, a modern NLP technique, for index construction.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count and temporal coverage are unknown, which may limit suitability assessment.
Provenance
- Source
- People's Daily articles.
- Collection Method
- Indexes derived using neural topic modeling.
- Time Range
- null
- Freshness
- Last update date is unknown; freshness unverified.
- Geography
- Likely China, given the source publication.