Indonesian Sentiment Analysis

A machine learning sentiment analysis for Indonesian text, built with Streamlit and scikit-learn.

---

🎯 About The Project

This project is a sentiment analysis application specifically designed for Indonesian language text. It analyzes user input and classifies the sentiment as Positive, Negative, or Neutral with confidence scores.

• Indonesian Language Focus: Specifically tailored for Indonesian text preprocessing
• Machine Learning: Uses Logistic Regression with TF-IDF vectorization
• Real-time Analysis: Instant sentiment prediction with confidence scores
• Comprehensive Preprocessing: Includes slang normalization, stemming, and stopword removal

---

✨ Features

Current Features

• Three-class Classification - Positive, Negative, and Neutral sentiments
• Confidence Scores - View probability distribution across all classes
• Text Preprocessing Pipeline
  • Twitter-specific cleaning (mentions, hashtags, URLs)
  • Emoji removal
  • Slang normalization
  • Stopword removal
  • Stemming (Sastrawi)
  • Capital ratio feature extraction

---

🎬 Demo on Local

---

🛠️ Technology Stack

Core Technologies

• Python 3.8+ - Programming language
• Streamlit - Web application framework
• scikit-learn - Machine learning library
• pandas - Data manipulation
• NLTK - Natural language processing

NLP Libraries

• Sastrawi - Indonesian stemmer
• TextBlob - Text processing
• NLTK Stopwords - Stopword removal

Machine Learning

• TF-IDF Vectorization - Text feature extraction
• Logistic Regression - Classification model
• scipy - Sparse matrix operations

---

📦 Installation

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)
• Git

Step-by-Step Installation

1. Clone the Repository

git clone https://github.com/AmeliaSyahla/sentiment-analysis.git
cd sentiment-analysis

2. Create Virtual Environment (Recommended)

# Windows
python -m venv .venv
.venv\Scripts\activate

# macOS/Linux
python3 -m venv .venv
source .venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Download NLTK Data

python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords')"

5. Prepare Required Files

Ensure you have these files in your project directory:

sentiment_analysis/
├── Assets/
│   ├── Fix_Final_Berita_dan_Tweet.csv     # Your dataset
│   ├── full_lexicon.csv                    # Sentiment lexicon
│   └── combined_slang_words.txt           # Slang dictionary (optional)
├── app.py
├── train_model.py
└── requirements.txt

---

🚀 Usage

Training the Model

Before using the application, you need to train the sentiment analysis model:

python train_model.py

This will generate:

• model.pkl - Trained Logistic Regression model
• vectorizer.pkl - TF-IDF vectorizer
• model_config.json - Model configuration

Running the Web Application

streamlit run app.py

The application will open in your default browser at http://localhost:8501

---