VAP-GameController at SemEval-2026 Task 2: Lexical-based and Emotion-Aware Approaches for Longtitudinal Emotion Prediction

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

Summary

The VAP-GameController team participated in SemEval-2026 Task 2, focusing on predicting continuous valence and arousal trajectories from longitudinal ecological essays. To model fine-grained emotional dynamics, the team explored three distinct approaches. These included hierarchical encoder-based models designed to capture contextual emotional patterns, a lexicon-based pipeline incorporating linguistic rules and a dual-level calibration mechanism for personalized estimation, and a hybrid framework that integrates lexical emotional signals directly into neural encoders. Experiments conducted on the official dataset, and evaluated using Pearson correlation (r) and Mean Absolute Error (MAE), demonstrated consistent improvements over established baseline methods. This research highlights the complementary strengths derived from combining neural representations with carefully calibrated lexical features for emotion prediction.

Key takeaway

For NLP Engineers developing emotion prediction systems, consider integrating both neural and lexical approaches. Your models for continuous valence and arousal prediction can achieve consistent improvements. Combine hierarchical encoders for contextual patterns with calibrated lexicon-based features for personalized estimation. This hybrid strategy offers a robust path to enhance the accuracy of fine-grained emotional dynamics from textual data.

Key insights

Combining neural encoders with calibrated lexical features improves continuous valence and arousal prediction from essays.

Principles

• Emotional dynamics benefit from contextual modeling.
• Lexical features offer personalized emotional estimation.
• Hybrid approaches enhance emotion prediction accuracy.

Method

The method involves hierarchical encoder-based models, a lexicon-based pipeline with linguistic rules and dual-level calibration, and a hybrid framework integrating lexical signals into neural encoders for valence/arousal prediction.

In practice

• Use hierarchical encoders for contextual emotion.
• Implement lexicon-based calibration for personalization.
• Integrate lexical features into neural models.

Topics

• Emotion Prediction
• Valence-Arousal Model
• SemEval-2026 Task 2
• Neural Encoders
• Lexical Features
• Longitudinal Data

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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