File size: 2,200 Bytes
c061ce5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | # Technical Specification: Depression Detection Using Tweets
## Architectural Overview
**Depression Detection System** is a Machine Learning application designed to analyze text data from tweets to predict depressive sentiment. The application utilizes a Support Vector Machine (SVM) model combined with Natural Language Processing (NLP) techniques to provide real-time classification through a modern web interface.
### Analysis Logic Flow
```mermaid
graph TD
Start["User Input"] --> Clean["Text Cleaning (spaCy)"]
Clean --> Vector["Vectorization (TF-IDF)"]
Vector --> Predict["SVM Prediction"]
Predict -->|Result| Render["Display Analysis"]
Render --> Reset["Ready for New Input"]
```
---
## Functional Components
### 1. Core Analysis Logic
- **Model Architecture**: Linear Support Vector Machine (SVM) optimized for high-dimensional text classification.
- **Preprocessing Pipeline**: Tokenization, lemmatization, and stop-word removal powered by the `en_core_web_lg` spaCy model.
- **Feature Extraction**: TF-IDF vectorization to convert cleaned text into numerical features for model inference.
### 2. Web Application Layer
- **Framework**: Flask-based micro-services architecture for handling HTTP requests and model serving.
- **Static Assets**: Localized sound effects, iconography (SVG), and CSS animations to ensure reliable offline performance.
- **Security**: Client-side protections including disabled right-click context menus and text selection to preserve interface integrity.
### 3. Deployment Pipeline
- **Standalone Execution**: Designed for local deployment using standard Python environments, managed via a comprehensive `requirements.txt`.
- **Asset Management**: Centralized `assets/` directory containing the serialized model, vectorizer, and linguistic datasets.
---
## Technical Prerequisites
- **Runtime**: Python 3.9+ with `pip` package manager.
- **Linguistic Data**: Pre-downloaded `en_core_web_lg` model for spaCy processing.
- **Resources**: Moderate RAM (approx. 2GB) for loading the large NLP model into memory.
---
*Technical Specification | Python | Version 1.0*
|