SemEval-2026 Task 2: Predicting Variation in Emotional Valence and Arousal over Time from Ecological Essays

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Natural Language Processing · Depth: Expert, quick

Summary

SemEval-2026 Task 2 focused on predicting emotional valence and arousal variations over time from ecological essays. This shared task utilized a longitudinal dataset collected across seven 14-day phases from 2021 to 2024. The data comprises real-time essays and feeling words written in English by U.S. service-industry workers, each associated with self-reported valence (0-4, highly negative to highly positive affect) and arousal (0-2, low to high energy) scores. The task included three subtasks: Longitudinal Affect Assessment, and forecasting affect variation as both state and disposition changes. Over 200 members registered, with 31 teams submitting official systems and 28 teams publishing system description papers. The task provided baseline results and insights from participating systems, analyzing performance differences between essays and feeling words, and assessing author-dependent performance. The datasets are publicly available.

Key takeaway

For NLP Engineers and Research Scientists developing affect prediction models, this SemEval task highlights the importance of longitudinal data and distinguishing between state and dispositional emotional changes. You should explore the publicly available datasets to benchmark new models, particularly focusing on performance variations between essay-length texts and individual feeling words, and assessing generalization to unseen authors. This can refine model robustness for real-world emotional intelligence applications.

Key insights

SemEval-2026 Task 2 evaluated models predicting longitudinal emotional valence and arousal from ecological essays and feeling words.

Principles

• Longitudinal data reveals affect dynamics.
• Distinguish state vs. disposition changes.
• Public datasets foster affect modeling.

Method

The task involved three subtasks: assessing longitudinal affect, and forecasting affect variation as both state and disposition changes using self-reported valence and arousal scores from worker essays.

In practice

• Analyze affect changes in time-series text.
• Compare model performance on essays vs. single words.
• Evaluate models on seen and unseen authors.

Topics

• Emotional Valence
• Emotional Arousal
• Longitudinal Data Analysis
• Affect Prediction
• SemEval Shared Task
• Natural Language Processing

Best for: AI Scientist, NLP Engineer, Research Scientist

Related on AIssential

• From Affect Prediction to Affect Forecasting: Evidence for Distinct Information Sources in Longitudinal Text — Current affect prediction relies on text semantics, while future affective change forecasting benefits from numeric trajectory dynamics.
• SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures — Evaluating LLM adaptability across diverse, low-resource language-culture pairs reveals critical performance and misalignment issues.
• Beyond Sentiment Classification: A Generative Framework for Emotion Intensity Evaluation in Text — Generative LLMs fine-tuned on continuous emotion intensity scores outperform classification and generalize to unseen affective dimensions.
• Audience Engagement with Arabic Women's Social Empowerment and Wellbeing: A Decadal Corpus — The corpus provides a decadal view of Arabic women's social empowerment discourse on Facebook.
• Automatic Evaluation of ENEM Essays: An Empirical Study on Linguistic and Contextual Representations — Hybrid models combining linguistic features and contextual embeddings improve Automatic Essay Scoring for Brazilian Portuguese Enem exams.
• A Chinese Education Broadcast Emotion Corpus — A new Chinese education broadcast emotion corpus aids emotion recognition research.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.