Multilingual Emotion classification using BERT (fine-tuning). Published at the WASSA workshop (ACL2022).

Overview

XLM-EMO: Multilingual Emotion Prediction in Social Media Text

Abstract

Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available emotion detection datasets across 19 languages. We train a multilingual emotion prediction model for social media data, XLM-EMO. The model shows competitive performance in a zero-shot setting, suggesting it is helpful in the context of low-resource languages. We release our model to the community so that interested researchers can directly use it.

See the paper for additional details:

Bianchi, F., Nozza, & D., Hovy. "XLM-EMO: Multilingual Emotion Prediction in Social Media Text". In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (Forthcoming). Association for Computational Linguistics, 2022. Link.


  • Free software: MIT license

Installing

pip install -U xlm-emo

Important: If you want to use CUDA you need to install the correct version of the CUDA systems that matches your distribution, see PyTorch.

Features

from xlm_emo.classifier import  EmotionClassifier
ec = EmotionClassifier()

ec.predict(["senti testa di cazzo", "I am very happy"])

>> ["anger", "joy"]

Models

Model Link Macro F1 on Test Set
XLM-EMO-T https://huggingface.co/MilaNLProc/xlm-emo-t 0.85
XLM-EMO-B TBD TBD
XLM-EMO-L TBD TBD

Reference

If you use this tool please cite the following paper:

@inproceedings{bianchi-etal-2022-xlmemo,
title = {{XLM-EMO}: Multilingual Emotion Prediction in Social Media Text},
author = "Bianchi, Federico and Nozza, Debora and Hovy, Dirk",
booktitle = "Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
year = "2022",
publisher = "Association for Computational Linguistics"
}

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

Owner
MilaNLP
MilaNLP
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