Error code:   ConfigNamesError
Exception:    KeyError
Message:      'feature'
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1031, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1004, in dataset_module_factory
                  ).get_module()
                    ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 605, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 386, in from_dataset_card_data
                  dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 317, in _from_yaml_dict
                  yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2031, in _from_yaml_list
                  return cls.from_dict(from_yaml_inner(yaml_data))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1876, in from_dict
                  obj = generate_from_dict(dic)
                        ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1463, in generate_from_dict
                  return {key: generate_from_dict(value) for key, value in obj.items()}
                               ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1478, in generate_from_dict
                  feature = obj.pop("feature")
                            ^^^^^^^^^^^^^^^^^^
              KeyError: 'feature'

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SemEval-2018 Task 1 (Preprocessed)

Dataset Description

This dataset contains a preprocessed and standardized version of SemEval-2018 Task 1: Affect in Tweets for multi-label emotion classification.

The original SemEval task focuses on emotion detection in tweets.
This version has been adapted to support multi-label learning and aligned with a unified emotion encoding scheme used across multiple benchmark datasets in this project.

Supported Tasks

Multi-label emotion classification
Emotion analysis in short social media texts
Cross-dataset benchmarking
Emotion representation learning

Dataset Structure

The dataset is split into:

train
validation
test

All splits follow the same schema.

Data Format

Each example consists of:

text (string): Preprocessed tweet text
labels (List[int]): Multi-one-hot encoded emotion labels

The label vector is 28-dimensional to maintain compatibility with other datasets.
Only 11 emotions are present in SemEval; all other emotion positions are set to 0.

Each label is binary:

1 → emotion present
0 → emotion absent

Multiple emotions may be active for a single sample.

Emotion Label Mapping

SemEval Emotion Set (11 Emotions)

Index	Emotion
0	Anger
1	Anticipation
2	Disgust
3	Fear
4	Joy
5	Love
6	Optimism
7	Pessimism
8	Sadness
9	Surprise
10	Trust

Unified Encoding Note

To support cross-dataset training and evaluation, SemEval labels are embedded into a 28-class emotion space.
Emotion classes not present in SemEval are encoded as absent (0).

Preprocessing Details

The following preprocessing steps were applied:

Conversion to multi-one-hot label encoding
Mapping to a unified 28-class emotion space
Removal of unused metadata and tweet-specific fields
Text normalization
Preprocessing applied prior to tokenization

Intended Use

This dataset is intended for:

Training and evaluating multi-label emotion classifiers
Emotion analysis of social media content
Cross-dataset generalization experiments
Benchmarking emotion representations

Limitations

The dataset contains preprocessed text only
Raw SemEval data is not included
Tweets may contain noise, slang, or informal language
Emotion annotations reflect annotator perception and task-specific definitions

Citation

If you use this dataset, please cite the original SemEval-2018 Task 1 paper:

@inproceedings{SemEval2018Task1,
  author    = {Mohammad, Saif M. and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
  title     = {SemEval-2018 {T}ask 1: {A}ffect in Tweets},
  booktitle = {Proceedings of the International Workshop on Semantic Evaluation (SemEval-2018)},
  address   = {New Orleans, LA, USA},
  year      = {2018}
}

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