| ---
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| language: tr
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| tags:
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| - sentiment-analysis
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| - turkish
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| - bert
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| - text-classification
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| license: apache-2.0
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| datasets:
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| - winvoker/turkish-sentiment-analysis-dataset
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| - WhiteAngelss/Turkce-Duygu-Analizi-Dataset
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| metrics:
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| - accuracy
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| - f1
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| - precision
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| - recall
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| ---
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|
|
| # Turkish Sentiment Analysis Model
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|
|
| A fine-tuned BERT model for Turkish sentiment analysis, trained on a combined dataset of 439,384 labeled Turkish sentences.
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|
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| ## Model Details
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|
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| - **Base Model:** `dbmdz/bert-base-turkish-cased`
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| - **Task:** Text Classification (Sentiment Analysis)
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| - **Language:** Turkish
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| - **Labels:** positive, negative, neutral
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|
|
| ## Training Data
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|
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| The model was trained on a combination of two high-quality Turkish sentiment datasets:
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| - `winvoker/turkish-sentiment-analysis-dataset` (440,641 samples)
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| - `WhiteAngelss/Turkce-Duygu-Analizi-Dataset` (440,641 samples)
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|
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| After deduplication and preprocessing, the final training set consisted of:
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| - **Training:** 351,507 samples
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| - **Validation:** 43,938 samples
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| - **Test:** 43,939 samples
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|
|
| ### Label Distribution
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|
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| - **Positive:** 234,957 (53.5%)
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| - **Neutral:** 153,809 (35.0%)
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| - **Negative:** 50,618 (11.5%)
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|
|
| ## Training
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|
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| - **Epochs:** 3
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| - **Learning Rate:** 2e-5
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| - **Batch Size:** 16
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| - **Max Length:** 128
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| - **Optimizer:** AdamW
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|
|
| ## Usage
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|
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| ```python
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| from transformers import AutoTokenizer, AutoModelForSequenceClassification
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| import torch
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|
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| # Load model and tokenizer
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| model_name = "codealchemist01/turkish-sentiment-analysis"
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| tokenizer = AutoTokenizer.from_pretrained(model_name)
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| model = AutoModelForSequenceClassification.from_pretrained(model_name)
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|
|
| # Example text
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| text = "Bu ürün gerçekten harika!"
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|
|
| # Tokenize
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| inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
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|
|
| # Predict
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| with torch.no_grad():
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| outputs = model(**inputs)
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| predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
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| predicted_label_id = predictions.argmax().item()
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|
|
| # Map to label
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| id2label = {0: "negative", 1: "neutral", 2: "positive"}
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| predicted_label = id2label[predicted_label_id]
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| confidence = predictions[0][predicted_label_id].item()
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|
|
| print(f"Label: {predicted_label}")
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| print(f"Confidence: {confidence:.4f}")
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| ```
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|
|
| ## Performance
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|
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| Evaluation metrics on the test set (43,939 samples):
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|
|
| - **Accuracy:** 97.45%
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| - **Weighted F1:** 97.42%
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| - **Weighted Precision:** 97.41%
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| - **Weighted Recall:** 97.45%
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|
|
| ### Per-Class Performance
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|
|
| | Class | Precision | Recall | F1-Score | Support |
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| |----------|-----------|--------|----------|---------|
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| | Negative | 91.42% | 86.69% | 88.99% | 5,062 |
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| | Neutral | 99.79% | 99.96% | 99.87% | 15,381 |
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| | Positive | 97.15% | 98.12% | 97.63% | 23,496 |
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|
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| **Note:** Negative class has lower performance due to class imbalance (only 11.5% of the dataset). The model performs excellently on neutral and positive classes.
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|
|
| ## Limitations
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|
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| - The model may not perform well on very short texts (< 3 words)
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| - Performance may vary across different domains (social media, news, reviews)
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| - Class imbalance may affect performance on minority classes (negative)
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|
|
| ## Citation
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|
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| If you use this model, please cite:
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|
|
| ```bibtex
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| @misc{turkish-sentiment-analysis,
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| title={Turkish Sentiment Analysis Model},
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| author={codealchemist01},
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| year={2024},
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| howpublished={\url{https://huggingface.co/codealchemist01/turkish-sentiment-analysis}}
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| }
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| ```
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|
|
| ## License
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|
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| Apache 2.0
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|
