https://github.com/dair-ai/emotion_dataset

:smile: Dataset for Emotion Recognition Research
https://github.com/dair-ai/emotion_dataset

dataset machine-learning nlp pytorch

Last synced: 3 days ago
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:smile: Dataset for Emotion Recognition Research

• Host: GitHub
• URL: https://github.com/dair-ai/emotion_dataset
• Owner: dair-ai
• Created: 2020-04-04T16:45:52.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-12-29T04:23:08.000Z (almost 2 years ago)
• Last Synced: 2024-04-14T02:53:41.560Z (7 months ago)
• Topics: dataset, machine-learning, nlp, pytorch
• Homepage:
• Size: 36.1 KB
• Stars: 186
• Watchers: 9
• Forks: 26
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# Emotion Dataset

This is a dataset that can be used for emotion classification. It has already been preprocessed based on the approach described in our [paper](https://www.aclweb.org/anthology/D18-1404/). It is also stored as a pandas dataframe and ready to be used in an NLP pipeline.

Note that the version of the data provided here corresponds to a six emotions variant that's meant to be used for educational and research purposes. 

## Download 

Hugging Face: https://huggingface.co/datasets/emotion

Download link: https://www.icloud.com/iclouddrive/084E9TMZ_lykn3QhU-kIX1DDQ#merged_training

Papers with Code Public Leaderboad: https://paperswithcode.com/sota/text-classification-on-emotion

## Load the Dataset Using Pandas

```python

import pandas as pd

df = pd.read_pickle("merged_training.pkl")

```

## Notebooks

Here is a [notebook](https://colab.research.google.com/drive/1nwCE6b9PXIKhv2hvbqf1oZKIGkXMTi1X#scrollTo=t23zHggkEpc-) showing how to use it for fine-tuning a pretrained language model for the task of emotion classification.

Here is another [notebook](https://colab.research.google.com/drive/176NSaYjc2eeI-78oLH_F9-YV3po3qQQO?usp=sharing) which shows how to fine-tune T5 model for emotion classification along with other tasks.

Here is also a hosted [fine-tuned model](https://huggingface.co/mrm8488/distilroberta-base-finetuned-sentiment) on HuggingFace which you can directly use for inference in your NLP pipeline. 

Feel free to reach out to me on [Twitter](https://twitter.com/omarsar0) for more questions about the dataset.

## Usage 

The dataset should be used for educational and research purposes only. If you use it, 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.",

}

```