| ---
|
| language:
|
| - ru
|
| - en
|
| license: apache-2.0
|
| task_categories:
|
| - text-classification
|
| dataset_info:
|
| - config_name: simplified_ekman
|
| features:
|
| - name: ru_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: 103759867.36530161
|
| num_examples: 333447
|
| - name: validation
|
| num_bytes: 12970022.317349194
|
| num_examples: 41681
|
| - name: test
|
| num_bytes: 12970022.317349194
|
| num_examples: 41681
|
| download_size: 68831057
|
| dataset_size: 129699912.0
|
| 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 Russian
|
|
|
| The original dataset: [Emotions](https://doi.org/10.34740/kaggle/dsv/7563141)
|
|
|
| The derived dataset was machine translated from English into Russian 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="ru")
|
|
|
| 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["ru_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-ru
|
|
|
| ## 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). |
