Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
multi-class-classification
Languages:
English
Size:
100K - 1M
Tags:
emotion-classification
License:
| pretty_name: Emotion | |
| annotations_creators: | |
| - machine-generated | |
| language_creators: | |
| - machine-generated | |
| language: | |
| - en | |
| license: | |
| - other | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 10K<n<100K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - text-classification | |
| task_ids: | |
| - multi-class-classification | |
| paperswithcode_id: emotion | |
| train-eval-index: | |
| - config: default | |
| task: text-classification | |
| task_id: multi_class_classification | |
| splits: | |
| train_split: train | |
| eval_split: test | |
| col_mapping: | |
| text: text | |
| label: target | |
| metrics: | |
| - type: accuracy | |
| name: Accuracy | |
| - type: f1 | |
| name: F1 macro | |
| args: | |
| average: macro | |
| - type: f1 | |
| name: F1 micro | |
| args: | |
| average: micro | |
| - type: f1 | |
| name: F1 weighted | |
| args: | |
| average: weighted | |
| - type: precision | |
| name: Precision macro | |
| args: | |
| average: macro | |
| - type: precision | |
| name: Precision micro | |
| args: | |
| average: micro | |
| - type: precision | |
| name: Precision weighted | |
| args: | |
| average: weighted | |
| - type: recall | |
| name: Recall macro | |
| args: | |
| average: macro | |
| - type: recall | |
| name: Recall micro | |
| args: | |
| average: micro | |
| - type: recall | |
| name: Recall weighted | |
| args: | |
| average: weighted | |
| tags: | |
| - emotion-classification | |
| dataset_info: | |
| - config_name: split | |
| features: | |
| - name: text | |
| dtype: string | |
| - name: label | |
| dtype: | |
| class_label: | |
| names: | |
| '0': sadness | |
| '1': joy | |
| '2': love | |
| '3': anger | |
| '4': fear | |
| '5': surprise | |
| splits: | |
| - name: train | |
| num_bytes: 1741597 | |
| num_examples: 16000 | |
| - name: validation | |
| num_bytes: 214703 | |
| num_examples: 2000 | |
| - name: test | |
| num_bytes: 217181 | |
| num_examples: 2000 | |
| download_size: 740883 | |
| dataset_size: 2173481 | |
| - config_name: unsplit | |
| features: | |
| - name: text | |
| dtype: string | |
| - name: label | |
| dtype: | |
| class_label: | |
| names: | |
| '0': sadness | |
| '1': joy | |
| '2': love | |
| '3': anger | |
| '4': fear | |
| '5': surprise | |
| splits: | |
| - name: train | |
| num_bytes: 45445685 | |
| num_examples: 416809 | |
| download_size: 15388281 | |
| dataset_size: 45445685 | |
| duplicated_from: emotion | |
| # Dataset Card for "emotion" | |
| ## Table of Contents | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Homepage:** [https://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset) | |
| - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| - **Size of downloaded dataset files:** 3.95 MB | |
| - **Size of the generated dataset:** 4.16 MB | |
| - **Total amount of disk used:** 8.11 MB | |
| ### Dataset Summary | |
| Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper. | |
| ### Supported Tasks and Leaderboards | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Languages | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ## Dataset Structure | |
| ### Data Instances | |
| An example looks as follows. | |
| ``` | |
| { | |
| "text": "im feeling quite sad and sorry for myself but ill snap out of it soon", | |
| "label": 0 | |
| } | |
| ``` | |
| ### Data Fields | |
| The data fields are: | |
| - `text`: a `string` feature. | |
| - `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5). | |
| ### Data Splits | |
| The dataset has 2 configurations: | |
| - split: with a total of 20_000 examples split into train, validation and split | |
| - unsplit: with a total of 416_809 examples in a single train split | |
| | name | train | validation | test | | |
| |---------|-------:|-----------:|-----:| | |
| | split | 16000 | 2000 | 2000 | | |
| | unsplit | 416809 | n/a | n/a | | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| #### Who are the source language producers? | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Annotations | |
| #### Annotation process | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| #### Who are the annotators? | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Personal and Sensitive Information | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Discussion of Biases | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Other Known Limitations | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ## Additional Information | |
| ### Dataset Curators | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Licensing Information | |
| The dataset should be used for educational and research purposes only. | |
| ### Citation Information | |
| If you use this dataset, please cite: | |
| ``` | |
| @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", | |
| month = oct # "-" # nov, | |
| year = "2018", | |
| address = "Brussels, Belgium", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://www.aclweb.org/anthology/D18-1404", | |
| doi = "10.18653/v1/D18-1404", | |
| pages = "3687--3697", | |
| abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.", | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset. | |