https://github.com/farscent/gamadata-1
https://github.com/farscent/gamadata-1
scraping-data semantic-analysis twitter-sentiment-analysis
Last synced: about 2 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/farscent/gamadata-1
- Owner: Farscent
- License: mit
- Created: 2025-05-08T15:29:27.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-05-08T15:44:47.000Z (about 2 months ago)
- Last Synced: 2025-05-12T19:13:57.226Z (about 2 months ago)
- Topics: scraping-data, semantic-analysis, twitter-sentiment-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 6.35 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🧠 Sentiment Analysis on the 2025 Revision of Indonesia’s TNI Law
This repository contains a data science project that analyzes **public sentiment** regarding the **2025 revision of the TNI Law (RUU TNI)**, based on Twitter data collected during the discussion and ratification period.
## 📌 Project Objective
To collect, clean, and analyze social media opinions related to the **Revisi Undang-Undang TNI (RUU TNI)**, with the goal of understanding public perception and identifying dominant narratives and keywords across sentiment categories (positive, neutral, negative).
---
## 🧩 Project Structure
| Module | Description |
|--------|-------------|
| 📰 `1. TWT Scraping Twitter/` | Twitter scraping using keyword-based queries |
| 🧼 `2. TWT Data Cleaning/` | Preprocessing and cleaning of raw tweet data |
| 📊 `3. TWT Analysis & Visualization/` | Exploratory analysis, sentiment breakdown, and visualization of top keywords |
---
## 📂 Data Sources
- **Primary**: Twitter (via scraping using relevant keywords such as `RUU TNI`, `dwifungsi`, `militer`, `sipil`, etc.)
---
## 📌 Context
The 2025 **Revisi UU TNI** was passed on March 20, 2025, triggering widespread public criticism due to its **closed and rushed process**, as well as perceived threats to civil liberties. Key revised articles include:
- **Pasal 3**: Administrative updates
- **Pasal 7**: Expanded OMSP operations overseas & reduced DPR oversight
- **Pasal 8**: Greater military involvement in civilian space
- **Pasal 47**: More public positions open to active TNI officers
- **Pasal 53**: Raised retirement age limits
Revisions to **Articles 8 and 47** were heavily criticized for weakening civil supremacy.
---
## 📈 Key Findings
- **4472 tweets** were analyzed.
- **Sentiment distribution**:
- 🔴 Negative: **75.8%**
- ⚪ Neutral: **22.7%**
- 🟢 Positive: **1.5%**
### Top Keywords per Sentiment:
- ✅ *Positive*: "TNI", "dwifungsi", "RUU", "sipil"
- ⚪ *Neutral*: "TNI", "dwifungsi", "baru", "jadi", "DPR", "demo"
- ❌ *Negative*: "Tolak", "RUU", "TNI", "Indonesia"
---
## 🛠️ Methods & Tools
- Python (Pandas, Sastrawi, Sklearn, Matplotlib, etc.)
- Sentiment analysis with lexicon-based or model-based approach
- Tokenization and keyword frequency extraction
- Data visualization
---
## 📅 Timeline
| Stage | Description | Status |
|-------|-------------|--------|
| Twitter Scraping | Collect tweets on RUU TNI | ✅ Done |
| Data Cleaning | Clean and preprocess tweets | ✅ Done |
| Sentiment Analysis | Classify tweets into sentiment categories | ✅ Done |
| Visualization | Generate insights and graphs | ✅ Done |
| Documentation | Create media & report assets | ✅ Done |
---
## 🤝 Contributors
- Farhan Adiwidya Pradana
- Yusuf Imantaka Bastari
- Muhammad Javier
- Deira Aisya Rifani
- Danar Fathurahman
---
## 💬 Notes
This project is part of the **Gamadata-1 initiative** under **RISDAT BEM KM UGM**, aimed at using data science for social awareness and advocacy, particularly in supporting democratic oversight and civic participation in policymaking.