task_categories:text-classification task_ids:sentiment-classification annotations_creators:expert-generated language_creators:crowdsourced multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:unknown region:us
In this dataset, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others.
@inproceedings{chatterjee-etal-2019-semeval,
    title={SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text},
    author={Ankush Chatterjee and Kedhar Nath Narahari and Meghana Joshi and Puneet Agrawal},
    booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation},
    year={2019},
    address={Minneapolis, Minnesota, USA},
    publisher={Association for Computational Linguistics},
    url={https://www.aclweb.org/anthology/S19-2005},
    doi={10.18653/v1/S19-2005},
    pages={39--48},
    abstract={In this paper, we present the SemEval-2019 Task 3 - EmoContext: Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading ''Why don't you ever text me!'' we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. However, the context of dialogue can prove helpful in detection of the emotion. In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps. A training data set of 30160 dialogues, and two evaluation data sets, Test1 and Test2, containing 2755 and 5509 dialogues respectively were released to the participants. A total of 311 teams made submissions to this task. The final leader-board was evaluated on Test2 data set, and the highest ranked submission achieved 79.59 micro-averaged F1 score. Our analysis of systems submitted to the task indicate that Bi-directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class}
}
---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: emocontext
pretty_name: EmoContext
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': others
          '1': happy
          '2': sad
          '3': angry
  config_name: emo2019
  splits:
  - name: train
    num_bytes: 2433205
    num_examples: 30160
  - name: test
    num_bytes: 421555
    num_examples: 5509
  download_size: 3362556
  dataset_size: 2854760
---

# Dataset Card for "emo"

## 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://www.aclweb.org/anthology/S19-2005/](https://www.aclweb.org/anthology/S19-2005/)
- **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:** 3.37 MB
- **Size of the generated dataset:** 2.85 MB
- **Total amount of disk used:** 6.22 MB

### Dataset Summary

In this dataset, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others.

### 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

#### emo2019

- **Size of downloaded dataset files:** 3.37 MB
- **Size of the generated dataset:** 2.85 MB
- **Total amount of disk used:** 6.22 MB

An example of 'train' looks as follows.
```
{
    "label": 0,
    "text": "don't worry  i'm girl hmm how do i know if you are what's ur name"
}
```

### Data Fields

The data fields are the same among all splits.

#### emo2019
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `others` (0), `happy` (1), `sad` (2), `angry` (3).

### Data Splits

| name  |train|test|
|-------|----:|---:|
|emo2019|30160|5509|

## 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

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Citation Information

```
@inproceedings{chatterjee-etal-2019-semeval,
    title={SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text},
    author={Ankush Chatterjee and Kedhar Nath Narahari and Meghana Joshi and Puneet Agrawal},
    booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation},
    year={2019},
    address={Minneapolis, Minnesota, USA},
    publisher={Association for Computational Linguistics},
    url={https://www.aclweb.org/anthology/S19-2005},
    doi={10.18653/v1/S19-2005},
    pages={39--48},
    abstract={In this paper, we present the SemEval-2019 Task 3 - EmoContext: Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading ''Why don't you ever text me!'' we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. However, the context of dialogue can prove helpful in detection of the emotion. In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps. A training data set of 30160 dialogues, and two evaluation data sets, Test1 and Test2, containing 2755 and 5509 dialogues respectively were released to the participants. A total of 311 teams made submissions to this task. The final leader-board was evaluated on Test2 data set, and the highest ranked submission achieved 79.59 micro-averaged F1 score. Our analysis of systems submitted to the task indicate that Bi-directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class}
}

```


### Contributions

Thanks to [@thomwolf](https://github.com/thomwolf), [@lordtt13](https://github.com/lordtt13), [@lhoestq](https://github.com/lhoestq) for adding this dataset.