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AI Sentiment Analysis of 225,000 Central Bank Sentences Across 26 Banks
Central banks shape global markets through their communications — but tracking policy language across 26 institutions is impossible manually. I built an NLP pipeline that classifies every sentence in central bank statements and minutes into hawkish/dovish sentiment.
Why Sentence-Level?
Most analysis works at the document level: "the Fed was hawkish." But a single statement might have hawkish inflation language, dovish labor guidance, and a dovish dissent — all in one document. Sentence-level classification captures these tensions.
The Hard Part
Central bank language is domain-specific enough that generic sentiment fails badly:
- "Future monetary policy decisions will be conditional on the inflation outlook" — sounds like guidance, but it's boilerplate. Correct: neutral.
- "The member voted against the rate increase" — naive model says dissent_hawkish. But the dissenter wanted lower rates: dissent_dovish.
- "Average interest rate on ruble loans rose to 8.5%" — looks like a rate hike, but it's a market description, not policy: neutral.
The pipeline uses LLM classification with bank-specific prompts and dual-temperature self-validation.
Current Divergences
- BOJ at 0.75% — cautiously hawkish, normalizing after decades at zero https://monetary.live/boj.html
- SNB at 0.00% — neutral, back to the floor https://monetary.live/snb.html
- TCMB at 37% — hawkish, emergency tightening https://monetary.live/tcmb.html
- PBOC at 3.00% — dovish, supporting growth https://monetary.live/pboc.html
- Fed at 3.75% — mixed, cutting but cautious language https://monetary.live/fed.html
Explore
- Dashboard — all 26 banks at a glance: https://monetary.live
- Statements — recent publications with AI summaries: https://monetary.live/statements.html
- Full: https://dev.to/aufklarer/building-an-nlp-pipeline-to-classify-225000-central-bank-sentences-gaf
Central banks shape global markets through their communications — but tracking policy language across 26 institutions is impossible manually. I built an NLP pipeline that classifies every sentence in central bank statements and minutes into hawkish/dovish sentiment.
Why Sentence-Level?
Most analysis works at the document level: "the Fed was hawkish." But a single statement might have hawkish inflation language, dovish labor guidance, and a dovish dissent — all in one document. Sentence-level classification captures these tensions.
The Hard Part
Central bank language is domain-specific enough that generic sentiment fails badly:
- "Future monetary policy decisions will be conditional on the inflation outlook" — sounds like guidance, but it's boilerplate. Correct: neutral.
- "The member voted against the rate increase" — naive model says dissent_hawkish. But the dissenter wanted lower rates: dissent_dovish.
- "Average interest rate on ruble loans rose to 8.5%" — looks like a rate hike, but it's a market description, not policy: neutral.
The pipeline uses LLM classification with bank-specific prompts and dual-temperature self-validation.
Current Divergences
- BOJ at 0.75% — cautiously hawkish, normalizing after decades at zero https://monetary.live/boj.html
- SNB at 0.00% — neutral, back to the floor https://monetary.live/snb.html
- TCMB at 37% — hawkish, emergency tightening https://monetary.live/tcmb.html
- PBOC at 3.00% — dovish, supporting growth https://monetary.live/pboc.html
- Fed at 3.75% — mixed, cutting but cautious language https://monetary.live/fed.html
Explore
- Dashboard — all 26 banks at a glance: https://monetary.live
- Statements — recent publications with AI summaries: https://monetary.live/statements.html
- Full: https://dev.to/aufklarer/building-an-nlp-pipeline-to-classify-225000-central-bank-sentences-gaf