Analisis-Sentimen-Film

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🎬 Analisis Sentimen Film

License: MIT [Python: 3.10+] Maintainer: Manavarya09 [Docs] Issues

📖 About

Analisis Sentimen Film is a reproducible repository for training and evaluating sentiment-analysis models on film reviews. It compares classical ML, deep learning, and transformer-based approaches (including LoRA/adapter methods) using standard datasets.

This repo is written in Indonesian and English for accessibility and includes notebooks, model results, and utilities to reproduce experiments.

✨ Highlights

  • ✅ Support for multiple model families (Logistic Regression, CNN, LSTM, Transformer)
  • ✅ Reproducible notebooks and saved model artifacts in /Result
  • ✅ Clear training/evaluation scripts and hyperparameter examples
  • ✅ Designed for quick experimentation on IMDB / local review datasets

📂 Repository layout

  • Notebooks/ — exploratory and reproducible Jupyter notebooks
  • Dataset Raw/ — raw data files and preprocessing notebooks
  • Models/ — model descriptions and checkpoints listing
  • Result/ — trained models, confusion matrices, word clouds, and evaluation outputs

🚀 Quick start

  1. Clone the repo
git clone https://github.com/Manavarya09/analisis-sentimen-film.git
cd analisis-sentimen-film
  1. Create a virtual environment and install dependencies (example):
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

If requirements.txt is missing, install common packages:

pip install numpy pandas scikit-learn tensorflow torch transformers jupyterlab matplotlib seaborn

🧪 Usage

  • Open Notebooks/Scraping_Film_ML_C.ipynb or Dataset Raw/Raw_Data_Machine_Learning.ipynb to explore data and preprocessing steps.
  • Run training scripts or notebooks to reproduce results. Example training command (pseudo):
python train.py --model transformer --dataset imdb --epochs 3 --batch-size 16 --lr 2e-5
  • Evaluation outputs (confusion matrices, accuracy, loss curves) are saved to Result/.

📊 Results & Models

Model performance and artifacts are inside /Result. See Result/Comparison Results/ and Result/Confusion Matrix/ for evaluation artifacts and visualizations.

🤝 Contributing

Contributions are welcome — open an issue or send a PR. Please include an explanation and a reproducible example for changes that affect models or data.

🧾 License

This project is licensed under the MIT License — see the LICENSE file for details.

Copyright (c) 2025 Manavarya09

📬 Contact

Maintainer: Manavarya09

If you'd like custom badges, more examples, or integration with CI/CD (GitHub Actions), tell me which ones and I'll add them.

Thank you for using Analisis Sentimen Film — contributions and feedback are appreciated!# Analisis-Sentimen-Film