Datasets:

jeffnyman
/

emotions

pretty_name: Emotions
license: cc-by-sa-4.0
language:
  - en
size_categories:
  - 10K<n<100K
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
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
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

Dataset Card for "emotions"

Dataset Description
- Dataset Summary
Dataset Structure
Additional Information
- Licensing Information
- Citation Information

Dataset Description

Paper: CARER: Contextualized Affect Representations for Emotion Recognition
Size of downloaded dataset files: 16.13 MB
Size of the generated dataset: 47.62 MB
Total amount of disk used: 63.75 MB

Dataset Summary

Emotions 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. Note that the paper does contain a larger data set with eight emotions being considered.

Dataset Structure

Data Instances

An example bit of data looks like this:

{
  "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 two configurations.

split: with a total of 20,000 examples split into train, validation and test.
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

Additional Information

Licensing Information

The dataset should be used for educational and research purposes only. It is licensed under Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

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.",
}