IndoBERT Sentiment Classifier for Social Media Posts – Universitas XYZ

This model is a fine-tuned IndoBERT transformer for performing sentiment analysis on Indonesian social media text (Twitter) related to university services. It classifies input text into positive, neutral, or negative sentiment categories.

🧠 Model Description

The model is built upon indobert-base-p2, a BERT-based transformer pre-trained on over 220 million Indonesian words. The fine-tuning process was done on 7500 samples containing balanced sentiment labels related to online academic services.

• Label classes: Positive, Neutral, Negative
• Preprocessing: Case folding, punctuation removal, stopword removal, stemming, tokenization (using IndoBERT tokenizer)

✅ Intended Use

• Analyzing Indonesian tweets about universities
• Sentiment-driven dashboards for academic service quality
• NLP applications in education sector

⚠️ Limitations

• Domain-specific to university-related sentiment
• May not generalize well to informal or slang-heavy text
• Sarcasm or mixed-sentiment detection is not supported
• Doesn’t handle toxicity or hate speech detection

📊 Dataset

• Source: Custom crawled tweets via keywords and hashtags (e.g. #telkomuniversity, Universitas XYZ)
• Size: 7500 samples
• Split: 70% train, 10% validation, 20% test
• Labels: 2500 positive, 2500 neutral, 2500 negative
• Language: Indonesian

⚙️ Training Procedure

Hyperparameters

• Learning rate: 5e-5
• Batch size: 8
• Epochs: 3
• Optimizer: Adam (β1=0.9, β2=0.999, ε=1e-8)
• Scheduler: Linear
• Seed: 42

Framework Versions

• Transformers: 4.24.0
• PyTorch: 1.13.0
• Tokenizers: 0.13.2

📈 Evaluation Metrics

Metric	Score
Accuracy	89%
F1 Score	88%
Precision	87%
Recall	89%

💻 Deployment Context

This model was integrated into a Django-based sentiment dashboard application with:

• A custom Twitter crawler
• Real-time sentiment classification
• Wordclouds and sentiment breakdowns by time period
• Admin tools for filtering, deleting, and exporting data

📄 Citation

If you use this model or its components, please cite:

@article{wijaya2023indobert,
  author    = {Kurniadi Ahmad Wijaya and Ade Romadhony and Donni Richasdy},
  title     = {Implementasi Model IndoBERT pada Dashboard Sentimen Media Sosial (Studi Kasus Universitas XYZ)},
  journal   = {eProceedings of Engineering},
  volume    = {10},
  number    = {4},
  year      = {2023},
  month     = {September},
  url       = {https://openlibrary.telkomuniversity.ac.id},
}