ma2za/many_emotions

Many Emotions

Many Emotions is a 2.7-million-row multilingual text-classification dataset for recognizing seven emotion categories in English, French, Italian, Spanish, and German. It combines examples from Emotion, DailyDialog, and GoEmotions.

The default unsplit corpus contains 2,710,740 non-empty rows derived from 550,123 source IDs. Every row records its source dataset and source-specific license.

The current release is 2.0.0. See CHANGELOG.md for changes from the original 2023 release.

No train/test split is implied. The default all configuration intentionally exposes one full partition. Create splits appropriate for your task and group them by source_id.

Labels

ID	Label	Rows	Share
0	anger	306,529	11.3%
1	fear	242,462	8.9%
2	joy	765,479	28.2%
3	love	186,061	6.9%
4	sadness	615,520	22.7%
5	surprise	94,298	3.5%
6	neutral	500,391	18.5%

Load the dataset

Install the required library:

pip install datasets

Load the complete corpus as one dataset:

from datasets import load_dataset

dataset = load_dataset("ma2za/many_emotions", split="full")

For exploration, tutorials, and integration tests, load the deterministic 10,000-row sample:

sample = load_dataset("ma2za/many_emotions", "sample", split="full")

The sample excludes known exact-text label conflicts and is balanced as evenly as possible across the 35 language-label combinations. It is not an evaluation split and should not be used as a benchmark.

all is the default configuration. Calling load_dataset("ma2za/many_emotions") without split="full" returns a DatasetDict containing that partition.

The repository retains the raw gzip file for archival access. Load it explicitly with:

raw = load_dataset(
    "json",
    data_files="hf://datasets/ma2za/many_emotions/data/many_emotions.json.gz",
    split="train",
)

The deprecated row-level gzip files also remain available for reproducibility, but they are not published as Hub configurations:

base = "hf://datasets/ma2za/many_emotions/data"
legacy = load_dataset(
    "json",
    data_files={
        "train": f"{base}/split_dataset_train.jsonl.gz",
        "validation": f"{base}/split_dataset_validation.jsonl.gz",
        "test": f"{base}/split_dataset_test.jsonl.gz",
    },
)

Both Hub configurations expose one partition named full. No repository code is executed when loading them.

Dataset structure

Default unsplit corpus

Partition	Rows
full	2,710,740

The default corpus is stored in 11 Zstandard-compressed Parquet shards. It adds explicit source_id and label_name columns and omits the single empty record found in the legacy files.

Balanced sample

Configuration	Partition	Rows	Purpose
sample	full	10,000	Fast exploration, tutorials, and integration tests

The sample contains 285 or 286 rows from each language-label combination and zero rows marked as exact-text label conflicts. Selection is deterministic by ID. Its source distribution is not balanced.

Deprecated legacy splits

Split	Rows
train	2,439,666
validation	135,538
test	135,537
Total	2,710,741

These splits are retained only for reproducibility. They are row-level splits, not source-grouped splits. Removing the final language suffix from id gives the source ID. Its membership across the legacy splits is:

Source ID membership	Source IDs
train only	327,997
validation only	141
test only	134
train and validation	99,233
train and test	99,487
validation and test	39
all three splits	23,092

Pairwise, train shares 122,325 source IDs with validation and 122,579 with test. Validation and test share 23,131 source IDs.

The default corpus contains approximately the same number of rows for each language:

Language	Code	Rows
English	en	550,122
German	de	542,598
Italian	it	541,145
Spanish	es	538,699
French	fr	538,176

Data fields

Field	Type	Description
id	string	Example identifier derived from the source and language.
source_id	string	Shared identifier for all retained language variants of a source example. Available in all.
text	string	Text to classify.
label	integer	Emotion class ID from 0 through 6.
label_name	string	Human-readable emotion label. Available in all.
dataset	string	Source dataset: emotion, daily_dialog, or go_emotions.
license	string	License or usage designation inherited from the source row.
language	string	ISO 639-1 language code: de, en, es, fr, or it.
is_label_conflict	boolean	Whether the exact (language, text) value has more than one label in the corpus.
label_variants	integer	Number of distinct labels associated with the exact (language, text) value.

Example:

{
  "id": "emotion_199458_en",
  "source_id": "emotion_199458",
  "text": "i live with a big wonderful family that wants me to feel welcomed so sitting alone in my room is not an option",
  "label": 2,
  "label_name": "joy",
  "dataset": "emotion",
  "license": "educational/research",
  "language": "en",
  "is_label_conflict": false,
  "label_variants": 1
}

Raw data

data/many_emotions.json.gz contains 598,298 records. Each record contains the English text in text and parallel French, Italian, Spanish, and German text in fr, it, es, and de. It also retains the original source label ID before normalization to the seven prepared labels above.

Unlike the prepared corpus, the raw file does not normalize labels or select one language column.

The prepared data retains 550,123 of the raw source IDs; 48,175 raw IDs are absent. Among retained source IDs, 530,398 have all five languages, while 19,725 have between one and four languages.

Source composition

Counts in the default unsplit corpus:

Source	Rows	Share	License value stored in rows
Emotion	2,055,918	75.8%	educational/research
DailyDialog	414,020	15.3%	CC BY-NC-SA 4.0
GoEmotions	240,802	8.9%	apache license 2.0

Data quality and limitations

The following results come from complete build-time and post-build scans:

• All 2,710,740 default rows have the documented typed schema, unique IDs, non-empty text, valid language codes, and labels between 0 and 6.
• The empty legacy record go_emotions_1_48976_es is excluded from all. Its corresponding Spanish field is also empty in the raw record.
• There are no repeated (language, text, label) triples in the default corpus.
• There are 159,897 exact (language, text) values associated with multiple labels, involving 323,049 rows. The same text can have as many as six labels. Use is_label_conflict and label_variants to identify them without recomputing this analysis.
• The deprecated legacy files place the same-language text across train and validation for 14,535 values, train and test for 14,660 values, and validation and test for 888 values. None has the same label on both sides of the measured pair.
• The deprecated legacy files also distribute translations of the same source example across splits. The default full partition has no split boundaries and makes no evaluation claim.
• Labels are imbalanced and strongly associated with source. For example, 83.2% of DailyDialog rows and 64.7% of GoEmotions rows are neutral, while the Emotion contribution contains no neutral rows.
• The translation method and translation-quality evaluation are not documented in this repository. Inspect the languages relevant to your use case before training or evaluation.
• Median text length is 15 to 16 whitespace-delimited words, depending on language; the 95th percentile is 39 to 42 words.

Source labels are normalized to anger, fear, joy, love, sadness, surprise, and neutral. The default corpus preserves the normalized labels from the legacy prepared files.

Reproducibility

reports/all_validation.json and reports/sample_validation.json record input and shard checksums, row counts, feature definitions, distributions, and conflict statistics.

Rebuild into a separate directory:

python scripts/build_all.py \
  --output-dir build/all \
  --sample-output-dir build/sample \
  --report build/all_validation.json \
  --sample-report build/sample_validation.json
python scripts/validate_all.py \
  --reports build/all_validation.json build/sample_validation.json

The builder concatenates the three legacy files into one partition, derives the additional fields, excludes empty text, annotates exact-text label conflicts, and writes deterministic Parquet files. It also selects the balanced sample by a stable hash of each ID. It does not assign train, validation, or test splits.

Licensing

This dataset combines sources with different licenses or usage designations. The license field preserves that information for each row. Users are responsible for following the terms associated with every source represented in their chosen subset; the repository-level metadata therefore uses license: other rather than applying one license to all rows.

Filter by source provenance or stored license value when a project cannot use every contribution:

go_emotions = dataset.filter(lambda row: row["dataset"] == "go_emotions")
apache_rows = dataset.filter(lambda row: row["license"] == "apache license 2.0")

These fields describe inherited provenance. Filtering them is not a legal conclusion about a resulting dataset or downstream use; review the linked upstream terms for your use case.

Citation

For the current version 2.0.0, cite:

Mazza, P. (2026). many_emotions (Revision abb7917) [Data set]. Hugging Face. https://doi.org/10.57967/hf/9639

@misc{paolo_mazza_2026,
  author = {paolo mazza},
  title = {many_emotions (Revision abb7917)},
  year = {2026},
  url = {https://huggingface.co/datasets/ma2za/many_emotions},
  doi = {10.57967/hf/9639},
  publisher = {Hugging Face}
}

Many Emotions version 1.0 was originally released in 2023. Its archived citation is:

Mazza, P. (2026). many_emotions (Revision 2f0d037) [Data set]. Hugging Face. https://doi.org/10.57967/hf/9638

@misc{paolo_mazza_2026_v1,
  author = {paolo mazza},
  title = {many_emotions (Revision 2f0d037)},
  year = {2026},
  url = {https://huggingface.co/datasets/ma2za/many_emotions},
  doi = {10.57967/hf/9638},
  publisher = {Hugging Face}
}

Also cite the applicable upstream datasets represented in the subset you use.

Upstream datasets

• Emotion: dataset and CARER paper (DOI).
• DailyDialog: dataset archive and paper.
• GoEmotions: dataset, Google Research repository, and paper (DOI).

@inproceedings{saravia-etal-2018-carer,
  title = {{CARER}: Contextualized Affect Representations for Emotion Recognition},
  author = {Saravia, Elvis and Liu, Hsien-Chi Toby and Huang, Yen-Hao and Wu, Junlin and Chen, Yi-Shin},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/D18-1404/},
  doi = {10.18653/v1/D18-1404},
  pages = {3687--3697}
}

@inproceedings{li-etal-2017-dailydialog,
  title = {{D}aily{D}ialog: A Manually Labelled Multi-turn Dialogue Dataset},
  author = {Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2017},
  publisher = {Asian Federation of Natural Language Processing},
  url = {https://aclanthology.org/I17-1099/},
  pages = {986--995}
}

@inproceedings{demszky-etal-2020-goemotions,
  title = {{G}o{E}motions: A Dataset of Fine-Grained Emotions},
  author = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/2020.acl-main.372/},
  doi = {10.18653/v1/2020.acl-main.372},
  pages = {4040--4054}
}