# XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection

arXiv: https://arxiv.org/abs/2011.01612 · HTML: https://mlsanity.com/paper/2011.01612/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 30 of 33)

- [A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias](https://mlsanity.com/paper/2404.00929/) (2024)
- [FinGPT: Large Generative Models for a Small Language](https://mlsanity.com/paper/2311.05640/) (2023)
- [Comparing Biases and the Impact of Multilingual Training across Multiple Languages](https://mlsanity.com/paper/2305.11242/) (2023)
- [BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages](https://mlsanity.com/paper/2502.11926/) (2025)
- [Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark](https://mlsanity.com/paper/2306.07902/) (2023)
- [SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection](https://mlsanity.com/paper/2503.07269/) (2025)
- [English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts](https://mlsanity.com/paper/2402.03223/) (2024)
- [PyPlutchik: visualising and comparing emotion-annotated corpora](https://mlsanity.com/paper/2105.04295/) (2021)
- [The Current State of Finnish NLP](https://mlsanity.com/paper/2109.11326/) (2021)
- [MMAFFBen: A Multilingual and Multimodal Affective Analysis Benchmark for Evaluating LLMs and VLMs](https://mlsanity.com/paper/2505.24423/) (2025)
- [The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages](https://mlsanity.com/paper/2310.14557/) (2023)
- [Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes](https://mlsanity.com/paper/2309.06517/) (2023)
- [Comparing Styles across Languages: A Cross-Cultural Exploration of Politeness](https://mlsanity.com/paper/2310.07135/) (2023)
- [Emotion Recognition and Generation: A Comprehensive Review of Face, Speech, and Text Modalities](https://mlsanity.com/paper/2502.06803/) (2025)
- [Transfer Learning for Multi-lingual Tasks -- a Survey](https://mlsanity.com/paper/2110.02052/) (2021)
- [Multi-aspect Multilingual and Cross-lingual Parliamentary Speech Analysis](https://mlsanity.com/paper/2207.01054/) (2022)
- [Label Alignment Regularization for Distribution Shift](https://mlsanity.com/paper/2211.14960/) (2022)
- [Guilt Detection in Text: A Step Towards Understanding Complex Emotions](https://mlsanity.com/paper/2303.03510/) (2023)
- [MaCmS: Magahi Code-mixed Dataset for Sentiment Analysis](https://mlsanity.com/paper/2403.04639/) (2024)
- [Enhancing Multi-Label Emotion Analysis and Corresponding Intensities for Ethiopian Languages](https://mlsanity.com/paper/2503.18253/) (2025)
- [SentiMM: A Multimodal Multi-Agent Framework for Sentiment Analysis in Social Media](https://mlsanity.com/paper/2508.18108/) (2025)
- [Why Are Some Emotions Harder for LLMs? Uncovering the Causal Mechanisms of Emotion Inference via Sparse Autoencoders](https://mlsanity.com/paper/2604.25866/) (2026)
- [Fear Is Not Always Fear: Variation in Emotion Label Equivalence Across Datasets](https://mlsanity.com/paper/2610.12841/) (2026)
- [Affect as a proxy for literary mood](https://mlsanity.com/paper/2304.02894/) (2023)
- [Croatian Film Review Dataset (Cro-FiReDa): A Sentiment Annotated Dataset of Film Reviews](https://mlsanity.com/paper/2305.08173/) (2023)
- [Emotion Identification for French in Written Texts: Considering their Modes of Expression as a Step Towards Text Complexity Analysis](https://mlsanity.com/paper/2405.14385/) (2024)
- [SentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment](https://mlsanity.com/paper/2411.18162/) (2024)
- [Performance Evaluation of Emotion Classification in Japanese Using RoBERTa and DeBERTa](https://mlsanity.com/paper/2505.00013/) (2025)
- [EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian](https://mlsanity.com/paper/2505.23297/) (2025)
- [FIN-bench-v2: A Unified and Robust Benchmark Suite for Evaluating Finnish Large Language Models](https://mlsanity.com/paper/2512.13330/) (2025)

## Abstract (arXiv)

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.