README.md

---

dataset_info:
- config_name: simplified_ekman
  features:
  - name: lv_text
    dtype: string
  - name: text
    dtype: string
  - name: labels
    dtype:
      list:
        class_label:
          names:
            - admiration
            - amusement
            - anger
            - annoyance
            - approval
            - caring
            - confusion
            - curiosity
            - desire
            - disappointment
            - disapproval
            - disgust
            - embarrassment
            - excitement
            - fear
            - gratitude
            - grief
            - joy
            - love
            - nervousness
            - optimism
            - pride
            - realization
            - relief
            - remorse
            - sadness
            - surprise
            - neutral
  - name: labels_ekman
    dtype:
      list:
        class_label:
          names:
            - anger
            - disgust
            - fear
            - joy
            - sadness
            - surprise
            - neutral
  - name: id
    dtype: string
  splits:
  - name: train
    num_bytes: 8267776
    num_examples: 43410
  - name: validation
    num_bytes: 1029817
    num_examples: 5426
  - name: test
    num_bytes: 1025790
    num_examples: 5427
  download_size: 6700239
  dataset_size: 10323383
configs:
- config_name: simplified_ekman
  data_files:
  - split: train
    path: simplified_ekman/train-*
  - split: validation
    path: simplified_ekman/validation-*
  - split: test
    path: simplified_ekman/test-*
license: apache-2.0
task_categories:
- text-classification
language:
- lv
- en
---


# Latvian GoEmotions dataset

The original dataset: [GoEmotions](https://huggingface.co/datasets/google-research-datasets/go_emotions) ([paper](https://aclanthology.org/2020.acl-main.372/)).

The derived dataset was machine translated from English into Latvian using the free Google Translate API (with [deep-translator](https://pypi.org/project/deep-translator/)). The translation script:

```python

from datasets import load_dataset

from deep_translator import GoogleTranslator

from deep_translator.exceptions import TranslationNotFound



original_dataset = load_dataset("go_emotions", name="simplified")

translator = GoogleTranslator(source="en", target="lv")



def translate_batch(batch):

    original_text = batch["text"]



    while True:

        try:

            translated_batch = translator.translate_batch(original_text)

            break

        except TranslationNotFound:

            print(f"Translation failed. Retrying...")



    # We fix untranslated entries (None values) by replacing them with the original text

    for i in range(len(translated_batch)):

        if not translated_batch[i]:

            translated_batch[i] = original_text[i]

            print(f"Replaced {original_text[i]} vs {translated_batch[i]}")



    batch["lv_text"] = translated_batch



    return batch



translated_dataset = original_dataset.map(

    translate_batch, batched=True, batch_size=500

)

```

The derived dataset uses two aligned tagsets:

- The original 27 + `neutral` emotion labels (may contain more than one label per sample):
```yaml

0: admiration

1: amusement

2: anger

3: annoyance

4: approval

5: caring

6: confusion

7: curiosity

8: desire

9: disappointment

10: disapproval

11: disgust

12: embarrassment

13: excitement

14: fear

15: gratitude

16: grief

17: joy

18: love

19: nervousness

20: optimism

21: pride

22: realization

23: relief

24: remorse

25: sadness

26: surprise

27: neutral

```

- The basic 6 + `neutral` emotion labels as per [Paul Ekman's theory](https://en.wikipedia.org/wiki/Emotion_classification) (may contain more than one label per sample):
```yaml

0: anger

1: disgust

2: fear

3: joy

4: sadness

5: surprise

6: neutral

```

Mapping from the 27 fine-grained emotions to the 6 basic emotions:

| GoEmotions | Ekman |
|---|---|
| admiration | joy |
| amusement | joy |
| anger | anger |
| annoyance | anger |
| approval | joy |
| caring | joy |
| confusion | surprise |
| curiosity | surprise |
| desire | joy |
| disappointment | sadness |
| disapproval | anger |
| disgust | disgust |
| embarrassment | sadness |
| excitement | joy |
| fear | fear |
| gratitude | joy |
| grief | sadness |
| joy | joy |
| love | joy |
| nervousness | fear |
| optimism | joy |
| pride | joy |
| realization | surprise |
| relief | joy |
| remorse | sadness |
| sadness | sadness |
| surprise | surprise |

## Acknowledgements

This work was supported by the EU Recovery and Resilience Facility project [Language Technology Initiative](https://www.vti.lu.lv) (2.3.1.1.i.0/1/22/I/CFLA/002).