XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection

TL;DR

XED is a multilingual, fine-grained emotion dataset with annotations for Finnish, English, and 30 additional languages, enabling improved sentiment analysis and emotion detection across diverse languages.

Contribution

The paper introduces XED, a new multilingual emotion dataset with annotations for multiple languages, including low-resource ones, and evaluates its effectiveness with language-specific models.

Findings

XED performs comparably to similar datasets in sentiment analysis.

The dataset covers 32 languages, including low-resource ones.

Evaluation shows XED's utility for emotion detection across languages.

Abstract

We introduce XED, a multilingual fine-grained emotion dataset. The dataset consists of human-annotated Finnish (25k) and English sentences (30k), as well as projected annotations for 30 additional languages, providing new resources for many low-resource languages. We use Plutchik's core emotions to annotate the dataset with the addition of neutral to create a multilabel multiclass dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to show that XED performs on par with other similar datasets and is therefore a useful tool for sentiment analysis and emotion detection.