ShinyQ/indobert-sentiment-analysis-indonesian-university-reviews

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

💻 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},
}