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
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_lgspaCy 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
pippackage manager. - Linguistic Data: Pre-downloaded
en_core_web_lgmodel for spaCy processing. - Resources: Moderate RAM (approx. 2GB) for loading the large NLP model into memory.
Technical Specification | Python | Version 1.0