
# Dataset Card for "emotions"

## Table of Contents

- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Additional Information](#additional-information)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)

## Dataset Description

- **Paper:** [CARER: Contextualized Affect Representations for Emotion Recognition](https://aclanthology.org/D18-1404/)
- **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.",
}
```

task_categories:text-classification task_ids:multi-class-classification size_categories:10K<n<100K language:en license:cc-by-sa-4.0 emotion-classification region:us
{"language": ["en"], "license": "cc-by-sa-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "Emotions", "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"}}]}]}
TAGS
#task_categories-text-classification #task_ids-multi-class-classification #size_categories-10K<n<100K #language-English #license-cc-by-sa-4.0 #emotion-classification #region-us 

Dataset Card for "emotions"
===========================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* 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:


### 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.



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).


If you use this dataset, please cite:



### 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: ### 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.



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).


If you use this dataset, please cite:
TAGS
#task_categories-text-classification #task_ids-multi-class-classification #size_categories-10K<n<100K #language-English #license-cc-by-sa-4.0 #emotion-classification #region-us 
 ### 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: ### 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.



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).


If you use this dataset, please cite: