---
language:
- en

license: apache-2.0

tags:
- semeval
- semeval-2026
- emotion
- affect-prediction
- temporal-nlp
- transformers
- roberta

datasets:
- semeval

pipeline_tag: text-classification

library_name: transformers

metrics:
- pearson-correlation
---

# 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

1. **Stability bias**
   - Temporal modeling smooths predictions and reduces sensitivity to abrupt changes.

2. **Dataset domain**
   - Data originates from a specific population (U.S. service-industry workers).

3. **Limited users**
   - Only **137 users** in training data.

4. **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

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# Citation

If you use this model, please cite: