AffectDynamics (Team AGI) — Longitudinal Affect Prediction Model
AffectDynamics is a temporal affect modeling system developed for SemEval-2026 Task 2: Predicting Variation in Emotional Valence and Arousal over Time from Ecological Essays.
The model predicts emotional valence and arousal from longitudinal text written by users across time. It combines transformer-based text encoding with temporal modeling and user-level conditioning to capture both stable emotional baselines and dynamic emotional changes.
Model Details
Model name: AffectDynamics-SemEval2026Task2
Developer: Harsh Rathva
Institution: Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat
Email: u24ai036@aid.svnit.ac.in
Architecture
The system consists of four main components:
1. Text Encoder
- RoBERTa-Large transformer encoder
- Produces contextual embeddings for each text input.
Different pooling strategies are used depending on text type:
- Essays → CLS / pooler representation
- Feeling word lists → mean pooled token embeddings
2. Temporal Encoder
- Unidirectional GRU
- Models longitudinal emotional dynamics across user timelines
- Ensures causal temporal modeling (no future information leakage)
3. User Conditioning
- Gated user embedding
- Uses user statistics such as:
- number of samples
- timeline length
- emotional entropy
This allows interpolation between user-specific and global representations.
4. Prediction Heads
| Task | Description |
|---|---|
| Subtask 1 (S1) | Absolute valence and arousal prediction |
| Subtask 2A (S2A) | Short-term emotional state change prediction |
| Subtask 2B (S2B) | Long-term dispositional change prediction |
Training Data
The model was trained using the official SemEval-2026 Task 2 dataset.
Dataset statistics
- Total texts: 5,285
- Training texts: 2,764
- Users: 182 total (137 training users)
- Time span: 2021–2024
Each entry contains:
| Field | Description |
|---|---|
| user_id | Anonymous user identifier |
| text | Ecological essay or feeling word list |
| timestamp | Time of writing |
| collection_phase | Study phase |
| valence | Emotional valence (-2 to 2) |
| arousal | Emotional arousal (0 to 2) |
The texts were written by U.S. service-industry workers describing their emotional state.
Training Details
Optimization
- Optimizer: AdamW
- Scheduler: OneCycleLR
- Batch size: 4
- Training epochs: 10
Learning Rates
| Component | Learning Rate |
|---|---|
| RoBERTa encoder | 2e-6 |
| GRU | 3e-4 |
| Task heads | 2e-5 |
Loss Functions
| Task | Loss |
|---|---|
| Subtask 1 | Ordinal regression with label smoothing |
| Subtask 2A | Smooth L1 loss |
| Subtask 2B | Mean squared error |
Evaluation Results
Official evaluation results from SemEval-2026 Task 2:
| Task | Metric | Valence | Arousal |
|---|---|---|---|
| Subtask 1 | Composite correlation | 0.600 | 0.452 |
| Subtask 2A | Pearson correlation | -0.167 | -0.147 |
| Subtask 2B | Pearson correlation | 0.086 | -0.081 |
The model demonstrates strong performance on absolute affect prediction, but exhibits limitations in change detection tasks, highlighting a trade-off between temporal stability and sensitivity to emotional transitions.
Intended Use
This model is intended for research purposes, including:
- longitudinal affect modeling
- emotion prediction from text
- temporal NLP modeling
- ecological momentary assessment analysis
Limitations
Stability bias
- Temporal modeling smooths predictions and reduces sensitivity to abrupt changes.
Dataset domain
- Data originates from a specific population (U.S. service-industry workers).
Limited users
- Only 137 users in training data.
Change prediction difficulty
- Predicting emotional deltas is harder than predicting absolute states.
Ethical Considerations
Emotion prediction models must be used responsibly.
Potential concerns include:
- privacy risks from modeling personal emotional data
- misuse for manipulation or surveillance
- dataset demographic bias
This model should not be used for clinical or psychological diagnosis.
Reproducibility
Code and training pipeline:
https://github.com/ezylopx5/AffectDynamics-SemEval2026Task2
Model weights:
https://huggingface.co/Haxxsh/AffectDynamics-SemEval2026Task2
Citation
If you use this model, please cite: