# EmoHopeSpeech: An Annotated Dataset of Emotions and Hope Speech in English and Arabic

arXiv: https://arxiv.org/abs/2505.11959 · HTML: https://mlsanity.com/paper/2505.11959/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 17 May 2025.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes](https://mlsanity.com/paper/2307.07961/) (2023)
- [PolyHope: Two-Level Hope Speech Detection from Tweets](https://mlsanity.com/paper/2210.14136/) (2022)

## Abstract (arXiv)

This research introduces a bilingual dataset comprising 23,456 entries for Arabic and 10,036 entries for English, annotated for emotions and hope speech, addressing the scarcity of multi-emotion (Emotion and hope) datasets. The dataset provides comprehensive annotations capturing emotion intensity, complexity, and causes, alongside detailed classifications and subcategories for hope speech. To ensure annotation reliability, Fleiss' Kappa was employed, revealing 0.75-0.85 agreement among annotators both for Arabic and English language. The evaluation metrics (micro-F1-Score=0.67) obtained from the baseline model (i.e., using a machine learning model) validate that the data annotations are worthy. This dataset offers a valuable resource for advancing natural language processing in underrepresented languages, fostering better cross-linguistic analysis of emotions and hope speech.