I built this dataset and I run the dashboard linked at the bottom.
I have been crawling monetary policy communications from 26 central banks and labelling them at the sentence level. The whole thing is CC-BY-4.0.
What is in it:
- 225,101 annotated sentences across 15,055 documents, Feb 1995 to Aug 2026
- Policy statements, rate decisions, meeting minutes, press conference transcripts
- 12 sentiment labels: rate_hike, rate_cut, rate_hold, guidance_hawkish, guidance_dovish, dissent_hawkish, dissent_dovish, liquidity_ease, liquidity_tight, reserve_ease, reserve_tight, neutral
- 9 topic labels: inflation, interest_rate, economic_activity, labor_market, exchange_rate, credit, financial_stability, fiscal_policy, governance
- 21 source languages, with an English translation on every non-English sentence in text_en
- 19,387 economic indicator rows (policy rates, FX, CPI) so you can join labels against outcomes
- Parquet, loads with datasets.load_dataset
Sources are the central banks’ own sites (federalreserve.gov, ecb.europa.eu, boj.or.jp and so on). Every document keeps its source URL.
The taxonomy follows IMF Working Paper WP/25/109, “From Text to Quantified Insights”. Labels are model-generated with gpt-4o-mini rather than hand-annotated, so spot-check them for your bank and period if you are using this for anything that matters.
Dataset: https://huggingface.co/datasets/aufklarer/central-bank-communications Dashboard built on it: https://monetary.live
Happy to take criticism of the taxonomy, especially the dissent and guidanthe hardest to pin down.
submitted by /u/ivan_digital
[link] [comments]
