Financial Phrasebank Sentiment Data for NLP Research
Summary
This reference describes the Financial Phrasebank, a labeled collection of financial news sentences used to train or evaluate natural language processing models. Human annotators assign positive, neutral, or negative sentiment labels. The corpus is available at four agreement thresholds: the strictest subset retains sentences on which all annotators agree, while looser thresholds include more examples with less label consensus. The document identifies this dataset as a benchmark for financial sentiment classification and names uses including word-vector training, sentiment tracking, and transformer fine-tuning.
The reference explains the dataset’s on-disk variants, loader behavior, and an alternative process for retrieving and converting the source files into parquet format. It also records the academic citation and noncommercial, attribution, and share-alike license terms. This is a dataset guide rather than a trading strategy or evidence that sentiment signals predict returns. Its practical limits include the tradeoff between label agreement and sample count, and the restrictions on commercial use.
Key ideas
- Financial Phrasebank provides financial news sentences labeled as positive, neutral, or negative.
- Higher annotator agreement yields a smaller subset with more consistent labels.
- The corpus supports sentiment classification benchmarks and NLP model training or evaluation.
- The dataset is licensed for noncommercial research subject to attribution and share-alike requirements.
- Using the corpus alone does not establish that sentiment labels predict market returns.
Tags
Full text
# Text Reference Corpora
# Text Reference Corpora
Labeled text corpora used as training or evaluation data for NLP models.
Unlike `sec/` (filings we produce) or `news/` (news archives we mirror),
these are published academic datasets.
## Datasets
| Corpus | Size | Use case | Loader |
| --- | --- | --- | --- |
| Financial Phrasebank (Malo et al. 2014) | ~2,300–4,800 sentences depending on agreement level | Sentiment classification benchmark | `load_financial_phrasebank` |
## On-disk Layout
```
$ML4T_DATA_PATH/alternative/text/financial_phrasebank/
├── sentences_allagree.parquet # 100% agreement, ~2,264 rows (default)
├── sentences_75agree.parquet # ~3,453 rows
├── sentences_66agree.parquet # ~4,217 rows
└── sentences_50agree.parquet # ~4,846 rows
```
Total disk footprint: under 500 KB. First load triggers a one-time
HuggingFace download (~1-2 seconds).
## Financial Phrasebank
Academic sentiment benchmark: sentences from financial news labeled
positive/neutral/negative by human annotators. Four agreement levels
are published (100%, 75%, 66%, 50%); the `allagree` subset is the most
reliable but smallest.
**License**: Creative Commons Attribution-NonCommercial-ShareAlike 3.0
(`CC BY-NC-SA 3.0`). Free for academic and non-commercial research;
attribution to Malo et al. (2014) required.
### Download
```bash
# The loader downloads from HuggingFace on first call; no manual step required.
uv run python -c "from data import load_financial_phrasebank; df = load_financial_phrasebank(); print(df.shape)"
```
To pre-populate the cache manually:
```python
from huggingface_hub import hf_hub_download
import zipfile, polars as pl
from pathlib import Path
DATA = Path("$ML4T_DATA_PATH/alternative/text/financial_phrasebank")
zip_path = hf_hub_download("takala/financial_phrasebank",
"data/FinancialPhraseBank-v1.0.zip", repo_type="dataset")
label_map = {"negative": 0, "neutral": 1, "positive": 2}
rows = []
with zipfile.ZipFile(zip_path) as z, z.open(
"FinancialPhraseBank-v1.0/Sentences_AllAgree.txt"
) as f:
for line in f.read().decode("latin-1").strip().splitlines():
sentence, label = line.rsplit("@", 1)
rows.append({"sentence": sentence.strip(), "label": label_map[label.strip()]})
DATA.mkdir(parents=True, exist_ok=True)
pl.DataFrame(rows).write_parquet(DATA / "sentences_allagree.parquet")
```
### Loading
```python
from data import load_financial_phrasebank
# Default: 100% agreement subset (most reliable, ~2,264 sentences)
df = load_financial_phrasebank(agreement="100")
# Or lower agreement levels for more training data
df = load_financial_phrasebank(agreement="50")
```
**Reference**: Malo, P., Sinha, A., Korhonen, P., Wallenius, J., & Takala, P.
(2014). *Good debt or bad debt: Detecting semantic orientations in economic texts.*
Journal of the Association for Information Science and Technology, 65(4).
## Consumers
- Chapter 10 NB 01 (word2vec training)
- Chapter 10 NB 03 (sentiment evolution)
- Chapter 10 NB 04 (transformer fine-tuning)Shown in full with attribution under the source's licence. Licence: MIT
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.