---
library_name: tensorflow
tags:
- sentiment-analysis
- aspect-based-sentiment-analysis
- tensorflow
- keras
language:
- tr
metrics:
- accuracy
pipeline_tag: text-classification
datasets:
- Sengil/Turkish-ABSA-Wsynthetic
---


# 🇹🇷 Turkish Aspect-Based Sentiment Analysis (ABSA) – BiLSTM + Word2Vec

This model performs aspect-based sentiment analysis (ABSA) on Turkish sentences. Given a sentence and a specific aspect, it predicts the sentiment polarity (Negative, Neutral, Positive) associated with that aspect.

## 🧠 Model Details

- **Model Type:** BiLSTM (Bidirectional Long Short-Term Memory) + Word2Vec
- **Developer:** [Sengil](https://huggingface.co/Sengil)
- **Library:** Keras
- **Input Format:** `"Sentence [ASP] Aspect"`
- **Labels:** 0 = Negative, 1 = Neutral, 2 = Positive
- **Training Dataset:** [Sengil/Turkish-ABSA-Wsynthetic](https://huggingface.co/datasets/Sengil/Turkish-ABSA-Wsynthetic)

## 📊 Evaluation Results

The model achieved the following performance on the test set:

| Class    | Precision | Recall | F1-Score | Support |
|----------|-----------|--------|----------|---------|
| Negative | 0.89      | 0.91   | 0.90     | 896     |
| Neutral  | 0.70      | 0.64   | 0.67     | 140     |
| Positive | 0.92      | 0.92   | 0.92     | 1178    |
| **Overall** |           |        | **0.90** | 2214    |

- **Overall Accuracy:** 90%
- **Macro-Averaged F1-Score:** 83%
- **Weighted-Averaged F1-Score:** 90%

## 🚀 Usage Example

Download model from HF
```python
from huggingface_hub import hf_hub_download
import pickle
from tensorflow.keras.models import load_model

model_path = hf_hub_download(repo_id="Sengil/Turkish-ABSA-BiLSTM-Word2Vec", filename="absa_bilstm_model.keras")
tokenizer_path = hf_hub_download(repo_id="Sengil/Turkish-ABSA-BiLSTM-Word2Vec", filename="tokenizer.pkl")

# load model
model = load_model(model_path)

# load tokenizer
with open(tokenizer_path, "rb") as f:
    tokenizer = pickle.load(f)
````

Input preprocessing
```python
import re
import nltk
nltk.download('punkt')

def preprocess_turkish(text):
    text = text.lower()
    text = re.sub(r"http\S+|www\S+|https\S+", "<url>", text)
    text = re.sub(r"@\w+", "<user>", text)
    text = re.sub(r"[^a-zA-Z0-9çğıöşüÇĞİÖŞÜ\s]", " ", text)
    text = re.sub(r"(.)\1{2,}", r"\1\1", text)
    text = re.sub(r"\s+", " ", text).strip()
    return text
````

Predict the input
```python
import numpy as np
from tensorflow.keras.preprocessing.sequence import pad_sequences

def predict_sentiment(sentence, aspect, max_len=84):
    input_text = sentence + " [ASP] " + aspect
    cleaned = preprocess_turkish(input_text)
    tokenized = tokenizer.texts_to_sequences([cleaned])
    padded = pad_sequences(tokenized, maxlen=max_len, padding='post')
    
    pred = model.predict(padded)
    label = np.argmax(pred)
    labels = {0: "Negatif", 1: "Nötr", 2: "Pozitif"}
    return labels[label]
````

run
```python
sentence = "Manzara sahane evet ama servis rezalet."
aspect = "manzara"

predict = predict_sentiment(sentence, aspect)
print("predict:", predict)
````

## 🏋️‍♀️ Training Details

* **Embedding:** Word2Vec (dimension: 100)
* **Model Architecture:**

  * Embedding layer (initialized with pre-trained Word2Vec weights)
  * 2 x BiLSTM layers (each with 100 units, dropout: 0.3)
  * Conv1D layer (100 filters, kernel size: 5)
  * Global Max Pooling
  * Dense layer (16 units, ReLU activation)
  * Output layer (3 units, softmax activation)
* **Training Parameters:**

  * Loss Function: `sparse_categorical_crossentropy`
  * Optimizer: Adam
  * Epochs: 35 (with early stopping)
  * Batch Size: 128
  * Learning Rate: 1e-3 (adjusted dynamically with ReduceLROnPlateau)

## 📚 Training Data

The model was trained on the [Sengil/Turkish-ABSA-Wsynthetic](https://huggingface.co/datasets/Sengil/Turkish-ABSA-Wsynthetic) dataset, which comprises semi-synthetic Turkish sentences annotated for aspect-based sentiment analysis, particularly in the restaurant domain.

## ⚠️ Limitations

* Performance on the Neutral class is lower compared to other classes, possibly due to class imbalance in the training data.
* The model may struggle with rare or ambiguous aspects not well represented in the training set.
* Complex sentence structures or ironic expressions may affect the model's accuracy.

## 📄 Citation

```
@misc{turkish_absa_bilstm_word2vec,
  title  = {Turkish Aspect-Based Sentiment Analysis using BiLSTM + Word2Vec},
  author = {Sengil},
  year   = {2025},
  url    = {https://huggingface.co/Sengil/Turkish-ABSA-BiLSTM-Word2Vec}
}
```

## 📬 Contact

For questions or feedback, please reach out via [Hugging Face profile](https://huggingface.co/Sengil).