Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
English
Size:
10K<n<100K
Tags:
emotion-classification
License:
File size: 5,805 Bytes
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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"
## 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.",
}
```
|