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

TL;DR

EmoHopeSpeech tackles the scarcity of multilingual resources linking emotions and hope by introducing a bilingual Arabic-English corpus annotated for emotion intensity, complexity, and causes, along with hope-speech categories and subcategories. The dataset comprises 23,456 Arabic and 10,036 English entries, with reliable annotations (Fleiss' Kappa $0.75$–$0.85$) and a feasible baseline micro-F1 of $0.67$, enabling cross-linguistic NLP analyses. It demonstrates statistically significant associations between emotion features and hope speech and provides baseline evaluations using both classical and transformer-based models (notably AraBERT and BERT). The work includes a comprehensive data-collection and annotation pipeline, error analysis, ethics considerations, and a public release to support future cross-cultural affective computing research.

Abstract

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.