https://github.com/drogbadvc/seo-semantic-proximity
a project focusing on the semantic proximity of web pages
https://github.com/drogbadvc/seo-semantic-proximity
Last synced: 4 months ago
JSON representation
a project focusing on the semantic proximity of web pages
- Host: GitHub
- URL: https://github.com/drogbadvc/seo-semantic-proximity
- Owner: drogbadvc
- Created: 2024-05-10T19:57:12.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-10T20:15:30.000Z (about 1 year ago)
- Last Synced: 2024-12-31T03:12:43.946Z (6 months ago)
- Language: Python
- Size: 128 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# SEO-Semantic-Proximity
## Overview
SEO-Semantic-Proximity is a tool to measure and analyze the semantic proximity between a source URL and a batch of target URLs. It leverages the power of natural language processing (NLP) to provide insights into the semantic similarity of web pages.
## Features
- **Batch Processing:** Analyze multiple URLs at once.
- **Semantic Analysis:** Uses advanced NLP models to compute similarity.
- **Streamlit Dashboard:** User-friendly interface for easy interaction.
- **Performance Optimized:** Efficient handling of URL fetching and text processing.
## Demo
## Installation
1. **Clone the repository:**
```bash
git clone https://github.com/drogbadvc/SEO-Semantic-Proximity.git
cd SEO-Semantic-Proximity
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```
## Usage
1. **Run the Streamlit dashboard:**
```bash
streamlit run dashboard.py
```
2. Enter the source URL and target URLs:
- Source URL: The URL to compare against target URLs.
- Target URLs: List of URLs to compare with the source URL.
3. Submit to analyze:
- Click the Submit button to compute the semantic proximity