---
language:
- lt
- en
license: apache-2.0
task_categories:
- text-classification
dataset_info:
- config_name: simplified_ekman
  features:
  - name: lt_text
    dtype: string
  - name: text
    dtype: string
  - name: labels
    dtype:
      class_label:
        names:
          '0': sadness
          '1': joy
          '2': love
          '3': anger
          '4': fear
          '5': surprise
  - name: labels_ekman
    dtype:
      class_label:
        names:
          '0': anger
          '1': disgust
          '2': fear
          '3': joy
          '4': sadness
          '5': surprise
  splits:
  - name: train
    num_bytes: 71995184.8176143
    num_examples: 333447
  - name: validation
    num_bytes: 8999425.091192849
    num_examples: 41681
  - name: test
    num_bytes: 8999425.091192849
    num_examples: 41681
  download_size: 55584192
  dataset_size: 89994035.0
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-*
---

# Twitter Emotions dataset in Lithuanian

The original dataset: [Emotions](https://doi.org/10.34740/kaggle/dsv/7563141)

The derived dataset was machine translated from English into Lithuanian using the free Google Translate API (with [deep-translator](https://pypi.org/project/deep-translator/)). It also contains an additional `labels_ekman` column that maps the original emotion classes to the [Paul Ekman's classification](https://en.wikipedia.org/wiki/Emotion_classification).

The translation script:

```python
import pandas as pd
from deep_translator import GoogleTranslator
from deep_translator.exceptions import TranslationNotFound

# Loads the dataset and drops the ID column
df = pd.read_csv("text.csv").iloc[:, 1:]

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

def translate_samples(samples):
    texts = samples["text"].tolist()

    while True:
        try:
            translated = translator.translate_batch(texts)
            break
        except TranslationNotFound:
            print(f"Translation failed for '{texts}', retrying...")

    # Replaces None values with the original text if translation was not successful
    translated = [
        t if t is not None else orig
        for t, orig in zip(translated, texts)
    ]

    # Prints replacements
    for t, orig in zip(translated, texts):
        if t == orig:
            print(f"Replaced {orig} with {t}")

    samples["lt_text"] = translated
    return samples

# Apply batch translation
batch_size = 500
translated_dataset = df.groupby(df.index // batch_size, group_keys=False).apply(translate_samples)
```

The column `labels` contains the emotion classes of the original dataset:
```yaml
0: sadness
1: joy
2: love - not distinguished in the Ekman's classification
3: anger
4: fear
5: surprise
```

The column `labels_ekman` contains the corresponding Ekman's emotion classes:
```yaml
0: anger
1: disgust - omitted, since not used in the original dataset
2: fear
3: joy
4: sadness
5: surprise
```

The mapping from the original to the Ekman's classification is made as follows:

| Original | Ekman |
|---|---|
| sadness (0) | sadness (4) |
| joy (1) | joy (3) |
| love (2) | joy (3) |
| anger (3) | anger (0) |
| fear (4) | fear (2) |
| surprise (5) | surprise (5) |

## See also

https://huggingface.co/datasets/AiLab-IMCS-UL/go_emotions-lt

## 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).