Op-Fed

This repository hosts the code and data to support the paper Op-Fed: Opinion, Stance, and Monetary Policy Annotations on FOMC Transcripts Using Active Learning by Alisa Kanganis and Katherine A. Keith.

If you use this data or code, please cite our paper:

@inproceedings{kanganis2026,
  author    = {Kanganis, Alisa and Keith, Katherine A.},
  title     = {Op-Fed: Opinion, Stance, and Monetary Policy Annotations on FOMC Transcripts Using Active Learning},
  booktitle = {Findings of the Association for Computational Linguistics (ACL Findings)},
  year      = {2026},
  note      = {Forthcoming}
}

Code to replicate the paper is in this Github repo.

Corresponding author: Email Katie Keith, kak5@williams.edu

Data

The main dataset is in opfed_v1.csv (config aggregated) and we also provide the raw annotations for all three annotators in opfed_raw_v1.csv (config raw).

Please see Section B: Datasheets for Datasets in our paper for a detailed description of this dataset.

To load each subset:

from datasets import load_dataset
aggregated = load_dataset("kakeith406/op-fed", "aggregated")
raw = load_dataset("kakeith406/op-fed", "raw")

opfed_v1.csv

Columns:

• unique_id: Example 19811222_189_9. The first two numbers, e.g.,19811222_189, correspond to the id in ConvoKit. In ConvoKit, this is the transcript number, e.g., 19811222, followed by the utterance number, e.g., 189 (starting at index 1). In our unique_id, the third number, e.g., _9 corresponds to the sentence number (after sentence segmentation with spacy); this starts at index 1 for the first sentence.
• speaker: The name of the speaker, e.g., MR. TRUMAN
• sentence: The full text of the target sentence.
• utterance: The full utterance within which the target sentence exists.
• -5 sentences: The previous five sentences leading up to (but not including) the utterance of the target sentence. When this is across multiple utterances, we return a list of dictionaries. Each dictionary is an utterance with keys for the 'speaker' and the 'text' for the utterance. Example:
  • [{'speaker': 'MR. LAWARE.', 'text': 'With the momentum that he will gain by our acquiescence to [releasing the transcripts], he will then say: Well, this is what I want you to decide to do.'}, {'speaker': 'MR. ANGELL.', 'text': 'Absolutely.'}, {'speaker': 'MR. LAWARE.', 'text': ""He's going to back us right into a corner.""}, ... ]
• -200+ tokens: The previous 200 tokens (rounded up to the nearest sentence) leading up to (but not including) the utterance with the target sentence. If this is across utterances, we use the same list-dictionary format as -5 sentences.
• 1_opinion: Opinion label on the target sentence. Possible labels: yes, no, or ambiguous
• 2_mp: Monetary policy label on the target sentence. Possible labels: yes, no, or ambiguous
• 3_mp_context: Whether the 2_mp label needed additional context. Possible labels: sentence, utterance, -5 sentences, or -200+ tokens
• 4_stance_nli: StanceNLI labels on the target sentence. Possible labels: neutral, entailment, contradiction or ambiguous
• 5_stance_nli_context: Whether the 4_stance_nli label needed additional context. Possible labels: sentence, utterance, -5 sentences, or -200+ tokens

opfed_raw_v1.csv

This file contains the per-annotator labels combined into list form, prior to aggregation. This file contains the per-annotator labels for all three annotators in list form, prior to aggregation. Each label cell contains a stringified Python list of three values, one per annotator. For example, in the first row, 1_opinion column, the cell value is ['yes', 'yes', 'yes'] which means all three annotators labeled the target sentence as 'yes' for the opinion aspect.

Because this is a hierarchical schema, one could also have missing values for some of the annotators, e.g., ['yes', 'nan', 'yes'] meaning the second annotator ('nan' value) did not reach that annotation stage due to earlier annotation decisions.

Columns:

• unique_id: Same format as in opfed_v1.csv.
• sentence: The full text of the target sentence.
• 1_opinion: Per-annotator opinion labels. List of three values, each yes, no, ambiguous, or nan.
• 2_mp: Per-annotator monetary policy labels. List of three values, each yes, no, ambiguous, or nan.
• 3_mp_context: Per-annotator context needed for 2_mp. List of three values, each sentence, utterance, -5 sentences, -200+ tokens, or nan.
• 4_stance_nli: Per-annotator StanceNLI labels. List of three values, each neutral, entailment, contradiction, ambiguous, or nan.
• 5_stance_nli_context: Per-annotator context needed for 4_stance_nli. List of three values, each sentence, utterance, -5 sentences, -200+ tokens, or nan.