---
license: other
task_categories:
- text-classification
task_ids:
- multi-label-classification
language:
- en
multilinguality:
- monolingual
size_categories:
- n<1K
pretty_name: POCAAffectClassification
tags:
- poetry
- english
- affect
- emotion-classification
- multi-label-classification
- geneva-emotion-wheel
- mteb
- poetrymteb
- embedding-evaluation
annotations_creators:
- crowdsourced
source_datasets:
- POCA
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  default: true
dataset_info:
- config_name: default
  features:
  - name: id
    dtype: string
  - name: title
    dtype: string
  - name: author
    dtype: string
  - name: poem
    dtype: string
  - name: labels
    sequence: int64
  - name: label_names
    sequence: string
  - name: scores
    sequence: float64
  - name: n_annotators
    dtype: int64
  splits:
  - name: train
    num_examples: 228
  - name: test
    num_examples: 61
---

# POCAAffectClassification

Multi-label **affect / emotion classification** for English poetry (PoetryMTEB), derived from the [POCA](https://doi.org/10.17863/CAM.73749) dataset (Khan, Hopkins & Gunes, ACII 2021).

Poems are annotated on the **Geneva Emotion Wheel** (20 discrete affects, intensity 0–10) via Mechanical Turk; we binarize to multi-labels for embedding evaluation.

## Dataset Card

| Item | Description |
|------|-------------|
| **Source** | POCA supplementary data (`mturk/combined.csv` + `poems/`) |
| **Paper** | [Multi-dimensional Affect in Poetry (POCA) Dataset](https://doi.org/10.17863/CAM.73749) (ACII 2021); DOI [10.1109/ACII52823.2021.9597451](https://doi.org/10.1109/acii52823.2021.9597451) |
| **Languages** | English (`en`) |
| **Unit** | Full poem text |
| **Labels** | Multi-label subset of **20** affects |
| **Size** | train=228; test=61 (matched poems with text) |
| **Splits** | Stratified by primary (highest-mean) affect ≈ 80% / 20%, seed=42 |
| **Evaluation metrics** | Multi-label classification on embeddings: **macro/micro F1**, **Average Precision (AP)** |

### Label binarization (from score statistics)

MTurk scores are noisy (annotator std ≈ 2.6 on a 0–10 scale) and absolute thresholds leave many empty / over-dense label sets. We therefore use:

1. Aggregate **mean** score per affect across annotators for each poem.
2. Compute **within-poem z-scores**; keep affects with \(z \ge 1.0\).
3. Always include the **top-1** affect (guarantees ≥1 label).

Mean labels/poem ≈ 3.12.

## Label taxonomy (20)

| id | label_name | train | test | total |
|---:|------------|------:|-----:|------:|
| 0 | `Admiration` | 56 | 11 | 67 |
| 1 | `Amusement` | 98 | 26 | 124 |
| 2 | `Anger` | 7 | 2 | 9 |
| 3 | `Compassion` | 34 | 4 | 38 |
| 4 | `Contempt` | 13 | 4 | 17 |
| 5 | `Disappointment` | 39 | 11 | 50 |
| 6 | `Disgust` | 34 | 9 | 43 |
| 7 | `Fear` | 8 | 1 | 9 |
| 8 | `Guilt` | 14 | 5 | 19 |
| 9 | `Hate` | 10 | 2 | 12 |
| 10 | `Interest` | 10 | 1 | 11 |
| 11 | `Joy` | 131 | 33 | 164 |
| 12 | `Pleasure` | 35 | 9 | 44 |
| 13 | `Love` | 40 | 10 | 50 |
| 14 | `Contentment` | 41 | 13 | 54 |
| 15 | `Pride` | 32 | 8 | 40 |
| 16 | `Regret` | 16 | 4 | 20 |
| 17 | `Relief` | 23 | 3 | 26 |
| 18 | `Sadness` | 69 | 16 | 85 |
| 19 | `Shame` | 17 | 4 | 21 |

Codebook: `label_taxonomy.json`.

## Features

| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Example id |
| `title` | string | Poem title |
| `author` | string | Poet |
| `poem` | string | Full poem body |
| `labels` | list[int64] | Affect class indices |
| `label_names` | list[string] | Canonical affect names |
| `scores` | list[float64] | Mean MTurk intensities (length 20, taxonomy order) |
| `n_annotators` | int64 | Number of MTurk annotations aggregated |

## Construction method

1. Load MTurk `combined.csv`; group by `(title, Author)`; average the 20 affect columns.
2. Resolve poem text from `poems/` via normalized filename matching.
3. Binarize with within-poem \(z \ge 1.0\) + top-1.
4. Stratified train/test split by primary affect.

## Citation

```bibtex
@article{khan_hopkins_gunes_2021,
  title={Multi-dimensional Affect in Poetry (POCA) Dataset: Acquisition, Annotation and Baseline Results},
  url={https://www.repository.cam.ac.uk/handle/1810/326293},
  DOI={10.17863/CAM.73749},
  publisher={IEEE},
  author={Khan, Akbir and Hopkins, Jack and Gunes, Hatice},
  year={2021}
}
```

Also: https://doi.org/10.1109/ACII52823.2021.9597451

## License

Follow upstream POCA / Cambridge repository terms (research use; rights reserved by authors/publisher unless otherwise noted).