title: Depression Detection Using Tweets
emoji: 🧠
colorFrom: blue
colorTo: indigo
sdk: docker
app_file: app.py
pinned: false
license: mit
short_description: Depression Detection in Tweets ML Web App
Depression Detection Using Tweets
A modern Python + Flask application designed to analyze tweet sentiment and predict depressive characteristics using a finalized SVM model and spaCy NLP pipeline.
Authors · Overview · Features · Structure · Results · Quick Start · Usage Guidelines · License · About · Acknowledgments
🤝🏻 Special Acknowledgement
Special thanks to Mega Satish for her meaningful contributions, guidance, and support that helped shape this work.
Overview
Depression Detection Using Tweets is a specialized Machine Learning framework designed to translate complex linguistic patterns into empirical psychological insights. This repository prioritizes high-dimensional feature extraction and probabilistic classification to provide a robust baseline for sentiment analysis within the context of mental health monitoring.
- Linguistic Determinism: The system utilizes deep NLP preprocessing, including lemmatization and entity normalization, to ensure that the semantic core of a tweet is preserved regardless of slang or stylistic variation.
- Vector-Space Inference: By leveraging Support Vector Machines (SVM) and TF-IDF vectorization, the model maps textual input into a multi-dimensional hyperplane, enabling precise binary classification of depressive sentiment.
- Architectural Efficiency: The backend is architected for low-latency serving via Flask, ensuring that model inference and result rendering occur in sub-second cycles, critical for interactive user feedback.
NLP Pipeline Optimization
To maximize classification reliability, the engine employs a multi-stage linguistic filter. Stop-word suppression and morphological analysis strip away structural noise, while the en_core_web_lg transformer model contextualizes surviving tokens. This ensures the classifier’s weights are strictly coupled with affective indicators, minimizing the false-positive skew common in generalized sentiment analysis models.
Features
| Feature | Description |
|---|---|
| Core SVM Model | High-Dimensional Classification engine optimized for binary depressive sentiment prediction. |
| NLP Pipeline | Deep linguistic feature extraction powered by the spaCy transformer model (en_core_web_lg). |
| Prediction Hub | Real-Time Inference Interface built with Flask for sub-second classification feedback. |
| Security Suite | Integrated Browser-Side Integrity protocols including anti-right-click and anti-select systems. |
| Cinematic Surprise | Immersive Branding Overlay featuring animated Twitter iconography and synchronized audio. |
Technical Polish: The Linguistic Singularity
We have engineered a Probabilistic Sentiment Manager that calibrates model weights across thousands of TF-IDF vectors to simulate human-like linguistic intuition. The visual language focuses on a "Neural Slate" aesthetic, ensuring maximum cognitive focus on the diagnostic outputs without procedural distraction.
Tech Stack
- Languages: Python 3.9+
- Logic: SVM Classifier (Scikit-Learn Inference Engine)
- Linguistic Data: spaCy NLP (Transformer-based word embeddings)
- Web App: Flask Framework (Micro-service architecture for model serving)
- UI System: Premium Modern Aesthetics (Custom CSS / Play Typography)
- Deployment: Standard Python Environment (PIP-managed dependencies)
Project Structure
DEPRESSION-DETECTION-USING-TWEETS/
│
├── docs/ # Technical Documentation
│ └── SPECIFICATION.md # Architecture & Design Specification
│
├── Mega/ # Archival Attribution Assets
│ ├── Filly.jpg # Companion (Filly)
│ └── Mega.png # Author Profile Image (Mega Satish)
│
├── screenshots/ # Project Visualization Gallery
│ ├── 01_landing_page.png # System Hub Initial State
│ ├── 02_footer_details.png # Brand and Metadata Footer
│ ├── 03_surprise_cinematic.png # Interactive Animated Sequence
│ ├── 04_predict_interface.png # Sentiment Analysis Entry Point
│ ├── 05_analysis_output.png # Model Inference result
│ └── 06_result_prediction.png # Final Sentiment Output
│
├── source_code/ # Primary Application Layer
│ ├── assets/ # Serialized Models & Linguistic Data
│ ├── core/ # ML Pipeline (Clean, Train, Predict)
│ ├── static/ # Styling, Audio, & Security Scripts
│ ├── templates/ # HTML Templates (Index, Result, 404)
│ └── app.py # Flask Application (Entry Point)
│
├── .gitattributes # Git configuration
├── .gitignore # Repository Filters
├── CITATION.cff # Scholarly Citation Metadata
├── codemeta.json # Machine-Readable Project Metadata
├── LICENSE # MIT License Terms
├── README.md # Comprehensive Scholarly Entrance
└── SECURITY.md # Security Policy & Protocol
Results
Minimalist interface for rapid tweet sentiment analysis.
Metadata Synthesis: Branding and Footer Detail
Scholarly attribution and project status integration.
Interactivity: Animated Twitter Sequence
Immersive audiovisual overlay triggered by core branding elements.
Sentiment Entry: Real-time Analysis Interface
Direct manipulation environment for high-latency textual input.
Model Inference: Feature Extraction Output
Deep linguistic analysis and probabilistic score generation.
Statistical Output: Final Sentiment Classification
Categorized classification results with immediate visual feedback.
Quick Start
1. Prerequisites
- Python 3.11+: Required for runtime execution. Download Python
- Git: For version control and cloning. Download Git
Data Acquisition & Memory Constraints
The linguistic pipeline relies on the en_core_web_lg transformer model, which requires an initial download of approximately 800MB. Ensure a stable network connection during setup. Additionally, loading this model into memory requires at least 2GB of available RAM to prevent swapping and ensure low-latency inference.
2. Installation & Setup
Step 1: Clone the Repository
Open your terminal and clone the repository:
git clone https://github.com/Amey-Thakur/DEPRESSION-DETECTION-USING-TWEETS.git
cd DEPRESSION-DETECTION-USING-TWEETS
Step 2: Configure Virtual Environment
Prepare an isolated environment to manage dependencies:
Windows (Command Prompt / PowerShell):
python -m venv venv
venv\Scripts\activate
macOS / Linux (Terminal):
python3 -m venv venv
source venv/bin/activate
Step 3: Install Core Dependencies
Ensure your environment is active, then install the required libraries:
pip install -r "Source Code/requirements.txt"
Step 4: Linguistic Model Acquisition
Download the large-scale linguistic model required for analysis (approx. 800MB):
python -m spacy download en_core_web_lg
3. Execution
Launch the sentiment analysis dashboard:
python "Source Code/app.py"
Usage Guidelines
This repository is openly shared to support learning and knowledge exchange across the academic community.
For Students
Use this project as reference material for understanding Support Vector Machines (SVM), spaCy NLP pipelines, and sentiment analysis within the context of mental health monitoring. The source code is available for study to facilitate self-paced learning and exploration of high-dimensional feature extraction and model serving via Flask.
For Educators
This project may serve as a practical lab example or supplementary teaching resource for Data Science, Natural Language Processing, and Machine Learning courses. Attribution is appreciated when utilizing content.
For Researchers
The documentation and architectural approach may provide insights into academic project structuring, psychological linguistic modeling, and algorithmic deployment.
License
This repository and all its creative and technical assets are made available under the MIT License. See the LICENSE file for complete terms.
Summary: You are free to share and adapt this content for any purpose, even commercially, as long as you provide appropriate attribution to the original authors.
Copyright © 2022 Amey Thakur & Mega Satish
About This Repository
Created & Maintained by: Amey Thakur & Mega Satish
This project features Depression Detection, a high-performance sentiment analysis system. It represents a personal exploration into Python-based machine learning and interactive web-service architecture.
Connect: GitHub · LinkedIn · ORCID
Acknowledgments
Grateful acknowledgment to Mega Satish for her exceptional collaboration and scholarly partnership during the development of this machine learning project. Her constant support, technical clarity, and dedication to software quality were instrumental in achieving the system's functional objectives. Learning alongside her was a transformative experience; her thoughtful approach to problem-solving and steady encouragement turned complex requirements into meaningful learning moments. This work reflects the growth and insights gained from our side-by-side academic journey. Thank you, Mega, for everything you shared and taught along the way.
Special thanks to the mentors and peers whose encouragement, discussions, and support contributed meaningfully to this learning experience.
Authors · Overview · Features · Structure · Results · Quick Start · Usage Guidelines · License · About · Acknowledgments
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