| --- |
| language: vi |
| tags: |
| - text-classification |
| - emotion-recognition |
| - vietnamese |
| license: mit |
| datasets: |
| - custom |
| metrics: |
| - accuracy |
| --- |
| |
| # 🇻🇳 Vietnamese Emotion Recognition (PhoBERT-based) |
| This repository provides a full pipeline for **Vietnamese emotion recognition**, including: |
| * 📊 Processed datasets |
| * 🧠 Training scripts for multiple models |
| * 💾 Trained model checkpoints |
| * 📈 Evaluation results |
|
|
| ## 📌 Overview |
| This project focuses on emotion classification in Vietnamese using both traditional and deep learning models, with a strong emphasis on **PhoBERT-base-v2**. |
| Key contributions: |
| * Build a **high-quality Vietnamese emotion dataset** |
| * Handle **class imbalance via oversampling** |
| * Compare multiple models (SVM → RNN → BiLSTM → CNN-LSTM → PhoBERT) |
| * Achieve **94.22% accuracy** with PhoBERT-base-v2 |
|
|
| ## 📂 Repository Structure |
| ``` |
| . |
| ├── bilstm_emotion_model/ # Saved BiLSTM model |
| ├── cnn_lstm_emotion_model/ # Saved CNN-LSTM model |
| ├── phobert_emotion_model/ # Saved PhoBERT model |
| ├── rnn_emotion_model/ # Saved RNN model |
| ├── svm_emotion_model/ # Saved SVM model |
| ├── flagged/ # Flagged or filtered samples |
| |
| ├── bilstm_best.keras # Best BiLSTM checkpoint |
| ├── cnn_lstm_best.keras # Best CNN-LSTM checkpoint |
| |
| ├── main_BILSTM.py # Train BiLSTM |
| ├── main_RNN_CNN-LSTM.py # Train RNN & CNN-LSTM |
| ├── main_lstm.py # LSTM training script |
| ├── main_phobert.py # Train PhoBERT |
| ├── main_svm.py # Train SVM |
| ├── main_v1.py # Legacy / combined script |
| ├── run.py # Main runner script |
| |
| ├── processed.xlsx # Main processed dataset |
| ├── processed_phobert.xlsx # Dataset for PhoBERT |
| ├── processed_svm.xlsx # Dataset for SVM |
| ├── train.xlsx # Training data |
| |
| ├── abbreviations.json # Text normalization rules |
| ├── word2vec_vi_syllables_100dims.txt # Word embeddings |
| |
| ├── requirements.txt |
| └── README.md |
| ``` |
| ## 📊 Dataset |
|
|
| ### 🔹 Sources |
| * Social media |
| * Product reviews |
| * Conversations |
|
|
| ### 🔹 Format |
| ```csv |
| sentence,emotion |
| "Tôi rất vui hôm nay",enjoyment |
| ``` |
|
|
| ### 🔹 Labels |
| * enjoyment |
| * anger |
| * sadness |
| * disgust |
| * fear |
| * surprise |
| * other |
|
|
| ### 🔹 Preprocessing |
| * Text cleaning and normalization |
| * Abbreviation expansion (`abbreviations.json`) |
| * Tokenization (Vietnamese-specific) |
| * Oversampling for class balance |
|
|
| ## 🧠 Models |
| | Model | Script | Output folder | |
| | -------- | ---------------------- | ------------------------- | |
| | SVM | `main_svm.py` | `svm_emotion_model/` | |
| | RNN | `main_RNN_CNN-LSTM.py` | `rnn_emotion_model/` | |
| | BiLSTM | `main_BILSTM.py` | `bilstm_emotion_model/` | |
| | CNN-LSTM | `main_RNN_CNN-LSTM.py` | `cnn_lstm_emotion_model/` | |
| | PhoBERT | `main_phobert.py` | `phobert_emotion_model/` | |
|
|
| 👉 PhoBERT performs best due to strong contextual understanding of Vietnamese language. |
|
|
| ## 🚀 Training |
|
|
| ### 🔧 Install dependencies |
| ```bash |
| pip install -r requirements.txt |
| ``` |
|
|
| ### ▶️ Run models |
|
|
| #### PhoBERT |
| ```bash |
| python main_phobert.py |
| ``` |
|
|
| #### SVM |
| ```bash |
| python main_svm.py |
| ``` |
|
|
| #### BiLSTM |
| ```bash |
| python main_BILSTM.py |
| ``` |
|
|
| #### RNN / CNN-LSTM |
| ```bash |
| python main_RNN_CNN-LSTM.py |
| ``` |
|
|
| #### Run all (if configured) |
| ```bash |
| python run.py |
| ``` |
|
|
| ## 📈 Results |
| | Model | Accuracy | |
| | -------- | ---------- | |
| | PhoBERT | **94.22%** | |
| | SVM | 78.69% | |
| | CNN-LSTM | 62.47% | |
| | BiLSTM | 59.56% | |
| | RNN | 30.02% | |
|
|
|
|
| ## 💾 Checkpoints |
|
|
| Pretrained models are stored in: |
|
|
| ``` |
| *_emotion_model/ |
| ``` |
|
|
| Example load: |
|
|
| ```python |
| from tensorflow.keras.models import load_model |
| |
| model = load_model("bilstm_best.keras") |
| ``` |
|
|
| ## 🧪 Example |
| ```python |
| text = "Hôm nay tôi rất vui" |
| prediction = model.predict(text) |
| print(prediction) |
| ``` |
|
|
| ## 📚 Publication |
| If you use this work, please cite: |
| **Advancing Emotion Recognition in Vietnamese: A PhoBERT-Based Approach for Enhanced Interaction** |
| 📄 DOI: [https://doi.org/10.34238/tnu-jst.12889](https://doi.org/10.34238/tnu-jst.12889) |
|
|
| ## 🔮 Future Work |
| * Multimodal emotion recognition (text + speech) |
| * Larger and more diverse datasets |
| * Real-time optimization |
| * Deployment in production systems |
|
|
| ## 📄 License |
| MIT License |
|
|
