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📸 Screenshots

1️⃣ Home Page — Start New Analysis

Users land on the main interface and choose to start a new analysis.

2️⃣ Platform Selection Modal

User selects the platform: Reddit, Instagram, or Twitter/X.

3️⃣ Enter Username / Profile URL

User inputs a public profile ID for analysis.

4️⃣ Login Page (Optional)

Optional login for storing analysis history.

5️⃣ Register Page

Simple registration form for account creation.

6️⃣Graph Insights — Sentiment & Abnormality Trends

Visual trends over time for abnormality and negative sentiment.

7️⃣ Per‑Post Detailed Sentiment & Abnormality Breakdown

Each scraped post includes sentiment scores and abnormality probability.

8️⃣ Dark Theme

Full analysis summary showing verdict, average sentiment, abnormality %, and graphs.

9️⃣ Analysis History Page

List of all previous analyses with quick metrics and PDF download links.

🔟 Generated PDF Report Preview

Professional PDF generated summarizing the analysis with charts.

MindSpace

MindSpace is a full‑stack web application that analyzes public social media posts for abnormality (risk) indicators and sentiment. It uses a dual‑engine approach (custom Naïve Bayes abnormality model + NLTK VADER sentiment scores) to produce interactive charts, per‑post breakdowns, and downloadable PDF reports. Registered users can save and revisit analysis history.

✅ Key Features

Platform & Scraping

• Reddit, Instagram, Twitter/X scraping (public posts)  
• Platform selector modal for quick analysis

Analysis & ML

• Custom Naïve Bayes abnormality model (model_nb.pkl)  
• VADER sentiment (neg/pos/neu)  
• Per‑post probability + sentiment tags

UI & Reports

• Interactive time series & bar charts  
• Per‑post cards with sentiment chips  
• Downloadable, printable PDF reports  
• Light + Dark UI themes

User & History

• Registration & login  
• Saved analyses with thumbnails and PDF links  
• Timestamps and platform metadata

💻 Tech Stack

Frontend: HTML, Bootstrap, JS (charts via Chart.js / Matplotlib for PDFs)  
Backend: Python (Flask)  
ML: scikit‑learn (Naïve Bayes), NLTK VADER  
DB: PostgreSQL / SQLite  
PDF: Matplotlib + ReportLab  
Scraping: AsyncPRAW, Apify, Playwright (where applicable)

⚙️ Setup & Quick Start

git clone https://github.com/SanjayMarathi/MindSpace.git
cd MindSpace
python -m venv venv
# activate venv
source venv/bin/activate  # mac/linux
# or .\venv\Scripts\activate  # windows
pip install -r requirements.txt
python app.py  # or `flask run` depending on entrypoint

Environment: create a .env with keys for any third‑party scraping services and DB connection string.

🧭 Usage

  1. Open the app in a browser.
  2. Click Start New Analysis → choose platform → enter a public username/profile URL.
  3. Wait for scraping and model processing — results page shows charts, per‑post cards and a PDF export button.
  4. Register / log in to save the analysis to your history.

Example analysis URL:

http://127.0.0.1:5000/analyze?platform=reddit&user=example_user

📁 Project Structure

MindSpace/
├── app.py
├── requirements.txt
├── models/
│   └── model_nb.pkl
├── static/
├── templates/
│   ├── index.html
│   ├── results.html
│   ├── history.html
│   └── report_pdf.html
├── scrapers/
│   ├── reddit_scraper.py
│   ├── instagram_scraper.py
│   └── twitter_scraper.py
└── utils/
    ├── pdf_utils.py
    └── viz_utils.py

📝 Developer Notes

  • Keep screenshots in outputs/ for README rendering on GitHub.
  • Place ML model files in models/ and load them from the analysis pipeline.
  • When deploying, ensure Matplotlib fonts are available to avoid PDF rendering issues.
  • Respect platform scraping policies and rate limits — use official APIs where required.

🧑‍💻 Author

Sanjay Marathi
GitHub: https://github.com/SanjayMarathi