Work

XLM-EMO: Multilingual Emotion Prediction in Social Media Text

Year: 2022

Type: conference-paper

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 lowresource languages.We release our model to the community so that interested researchers can directly use it.

Source: Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA)

DOI: 10.18653/v1/2022.wassa-1.18

Authors: Federico Bianchi, Debora Nozza, Dirk Hovy

Institution: Bocconi University

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: European Commission, Fondazione Cariplo, H2020 European Research Council

Awards: G434337789, Incorporating Demographic Factors into Natural Language Processing Models