Emotion Dataset for Emotion Recognition Tasks

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 below.

The authors constructed a set of hashtags to collect a separate dataset of English tweets from the Twitter API belonging to eight basic emotions, including anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. The data has already been preprocessed based on the approach described in their paper.

- **Homepage:** [https://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset)
- **Paper:** [CARER: Contextualized Affect Representations for Emotion Recognition](https://aclanthology.org/D18-1404/)
- **Point of Contact:** [e-mail]
- **Size of downloaded dataset files:** 3.95 MB
- **Size of the generated dataset:** 4.16 MB
- **Total amount of disk used:** 8.11 MB

An example of 'train' looks as follows.
```
{
"label": 0,
"text": "im feeling quite sad and sorry for myself but ill snap out of it soon"
}
```
### Starter Notebook
[Exploratory Data Analysis of the emotion dataset](https://www.kaggle.com/parulpandey/exploratory-data-analysis-of-the-emotion-dataset)

### Acknowledgements

```
@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.",
}
```

一项包含六种基本情绪（愤怒、恐惧、喜悦、爱情、悲伤和惊讶）的英文推特消息数据集。欲获取更详细信息，请参阅以下论文。作者构建了一系列标签，以通过Twitter API收集属于八种基本情绪（包括愤怒、期待、厌恶、恐惧、喜悦、悲伤、惊讶和信任）的英文推文数据集。数据已根据论文中描述的方法进行预处理。

- **主页:** [https://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset)
- **论文:** [CARER：基于上下文的情感表征用于情感识别](https://aclanthology.org/D18-1404/)
- **联系方式:** [e-mail]
- **下载数据集文件大小:** 3.95 MB
- **生成数据集大小:** 4.16 MB
- **磁盘总占用空间:** 8.11 MB

'训练'数据示例如下。

{
"label": 0,
"text": "我感到非常悲伤并为自己感到抱歉，但我会很快振作起来"
}

### 初步数据探索笔记本
[情感数据集的探索性数据分析](https://www.kaggle.com/parulpandey/exploratory-data-analysis-of-the-emotion-dataset)

### 致谢

@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 = "情感的表达方式细腻而多变，这取决于集体或个人的经历、知识和信仰。因此，为了理解通过文本传达的情感，需要一个能够捕捉和模拟不同语言细微差别和现象的强大机制。我们提出了一种半监督的基于图算法，以生成丰富的结构描述符，这些描述符作为构建基于文本的上下文情感表征的基石。基于模式的表征进一步通过词嵌入得到丰富，并通过多个情感识别任务进行评估。我们的实验结果表明，所提出的方法在情感识别任务上优于现有技术。"
}