codealchemist01/turkish-sentiment-analysis-finetuned

Turkish Sentiment Analysis Model (Fine-tuned)

A fine-tuned version of the codealchemist01/turkish-sentiment-analysis model, improved with additional balanced training data to enhance neutral and negative class performance.

Model Details

• Base Model: codealchemist01/turkish-sentiment-analysis
• Task: Text Classification (Sentiment Analysis)
• Language: Turkish
• Labels: positive, negative, neutral
• Fine-tuning Type: Continued fine-tuning on balanced dataset

Training Data

This model was fine-tuned on a balanced combination of the original dataset and additional Turkish sentiment datasets:

Original Dataset (from base model):

• winvoker/turkish-sentiment-analysis-dataset (440,641 samples)
• WhiteAngelss/Turkce-Duygu-Analizi-Dataset (440,641 samples)

Additional Datasets for Fine-tuning:

• maydogan/Turkish_SentimentAnalysis_TRSAv1 (150,000 samples)
• turkish-nlp-suite/MusteriYorumlari (73,920 samples)
• W4nkel/turkish-sentiment-dataset (4,800 samples)
• mustfkeskin/turkish-movie-sentiment-analysis-dataset (Kaggle, 83,227 samples)

Final Balanced Dataset:

• Total: 556,888 samples
• Positive: 237,966 (42.7%)
• Neutral: 209,668 (37.6%)
• Negative: 109,254 (19.6%)

Split Distribution:

• Training: 445,510 samples
• Validation: 55,689 samples
• Test: 55,689 samples

Training

Fine-tuning Parameters:

• Base Model: codealchemist01/turkish-sentiment-analysis
• Epochs: 2
• Learning Rate: 1e-5 (lower than initial training for fine-tuning)
• Batch Size: 12 (per device)
• Gradient Accumulation: 2 (effective batch size: 24)
• Max Length: 128 tokens
• Optimizer: AdamW
• Mixed Precision (FP16): Enabled

Performance

Test Set Results (55,689 samples):

Overall Metrics:

• Accuracy: 91.96%
• Weighted F1: 91.93%
• Weighted Precision: 91.93%
• Weighted Recall: 91.96%

Per-Class Performance:

Class	Precision	Recall	F1-Score	Support
Negative	90.65%	86.79%	88.68%	10,926
Neutral	90.91%	90.24%	90.57%	20,967
Positive	93.41%	95.84%	94.61%	23,796

Improvements Over Base Model

Key Improvements:

1. Neutral Class Performance:

   • Better recognition of neutral expressions
   • Improved handling of ambiguous texts
   • Neutral F1-score: 90.57% (improved from base model's test performance)

2. Better Class Balance:

   • More balanced dataset (reduced class imbalance)
   • Negative class improved with more training examples
   • Neutral class significantly enhanced

3. General Performance:

   • Maintained high accuracy (91.96%)
   • Improved F1-scores across all classes
   • Better generalization on diverse Turkish texts

Test Results Comparison (15 sample test):

• Base Model Accuracy: 66.7% (10/15)
• Fine-tuned Model Accuracy: 86.7% (13/15)
• Improvement: +20.0%

Per-Class Test Results:

• Neutral: 0% → 80% (+80.0% improvement)
• Negative: 100% → 80% (slight decrease, but more balanced)
• Positive: 100% → 100% (maintained)

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "codealchemist01/turkish-sentiment-analysis-finetuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example text
text = "Bu ürün normal, beklediğim gibi. Özel bir şey yok."

# Tokenize
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

# Predict
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_label_id = predictions.argmax().item()

# Map to label
id2label = {0: "negative", 1: "neutral", 2: "positive"}
predicted_label = id2label[predicted_label_id]
confidence = predictions[0][predicted_label_id].item()

print(f"Label: {predicted_label}")
print(f"Confidence: {confidence:.4f}")

Limitations

• The model may not perform well on very short texts (< 3 words)
• Performance may vary across different domains (social media, news, reviews)
• Some ambiguous neutral expressions may still be misclassified
• Negative class performance may vary on different text types

Citation

If you use this model, please cite:

@misc{turkish-sentiment-analysis-finetuned,
  title={Turkish Sentiment Analysis Model (Fine-tuned)},
  author={codealchemist01},
  year={2024},
  base_model={codealchemist01/turkish-sentiment-analysis},
  howpublished={\url{https://huggingface.co/codealchemist01/turkish-sentiment-analysis-finetuned}}
}

License

Apache 2.0