auto-econ-sentiment 0.2.0

Lexical sentiment analysis pipeline for central bank and economic text data.

AutoEconSentiment

auto-econ-sentiment is a reproducible pipeline for measuring economic sentiment in central bank and financial text. The core package is lexical-first: it cleans text, scores it with established economic dictionaries, and exports comparable sentiment outputs. Transformer sentiment is available as an optional extension.

Quick Start

Install the base package:

uv sync

Run the default YAML-configured pipeline:

uv run python -m src.auto_econ_sentiment.pipeline

Or call the Python API:

from auto_econ_sentiment.pipeline import AutoEconSentiment

analyzer = AutoEconSentiment(
    import_file_path="data/raw/basic_tests/monetary_policy_statement.parquet.gzip",
    text_column="text",
    date_column="date",
    export_path="data/sentiment/basic_tests/",
)

analyzer.run(
    clean_config={"tokenize": True, "stem": True},
    dictionaries={"unstemmed": ["correa", "hubert", "lm", "hiv"], "stemmed": ["ap", "bn"]},
    aggregation_methods=["posneg", "allwords"],
    export_results=True,
)

Transformer Quick Start

Transformer support is optional so lexical users do not need to install torch or download Hugging Face models.

uv sync --extra transformers

Then enable transformer models in params.yaml:

models:
  transformer:
    enabled: true
    text_column_transformer: text_clean
    aggregation_methods: [bysentence]
    output_schema: shares
    models:
      - name: gtfintechlab/FOMC-RoBERTa
        short_name: fomc
        num_labels: 3
        label_mapping:
          LABEL_0: positive
          LABEL_1: negative
          LABEL_2: neutral
        sentiment_values:
          positive: 1
          negative: -1
          neutral: 0

The transformer examples in params.yaml show the supported model-list format. More experimental transformer notes live locally in docs/feedback/ when present.

Transformer runs export sentiment_transformer.parquet.gzip and, for sentence-level aggregation, sentiment_transformer_sentence_probabilities.parquet.gzip alongside the existing lexical and combined output tables.

What It Does

• Loads CSV or parquet text data.
• Cleans and normalizes economic text.
• Scores text with multiple central bank and financial dictionaries.
• Optionally scores text with transformer classifiers using explicitly configured label mappings.
• Exports cleaned text, matched words, counts, probabilities, and sentiment scores.
• Makes dictionary and model disagreement visible for research workflows.

Documentation

• Architecture
• Data and Outputs
• Examples
• Roadmap
• Transformer notebook

CBS Speeches Demo

Download the CBS central bank speeches dataset and run the sentiment pipeline:

uv run python -m src.data.cb_speeches_download
uv run python -m src.data.cb_speeches_clean

Then open notebooks/demo_cb_speechs.ipynb to explore the outputs.

Citations

Dataset:

• Campiglio, E., Deyris, J., Romelli, D., & Scalisi, G. (2025). Warning words in a warming world: Central bank communication and climate change. European Economic Review, 105101.

Lexical dictionaries:

• Loughran, T. and B. McDonald (2011). When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks. The Journal of Finance 66, 35-65.
• Correa, R., K. Garud, J. Londono, and N. Mislang (2017). Sentiment in Central Bank as Financial Stability Reports. International Finance Discussion Paper 1203.
• Hubert, P. and F. Labondance (2021). The signaling effects of central bank tone. European Economic Review 133, 103684.
• Stone, P. J., D. C. Dunphy, and M. S. Smith (1966). The General Inquirer: A Computer Approach to Content Analysis.
• Apel, M. and M. Blix Grimaldi (2014). How Informative Are Central Bank Minutes? Review of Economics 65(1), 53-76.
• Bennani, H. and M. Neuenkirch (2017). The (Home) Bias of European Central Bankers. Applied Economics 49(11), 1114-1131.