Estimating the Popularity of Tourist Sites in Senegal Using Social Media Data

Context / Motivation

Senegal has a rich touristic heritage (natural, historical, and cultural sites).
Analyzing social media data provides a way to estimate the real or perceived popularity of these sites.
This project aims to support:

• Tourism authorities in identifying the most attractive and underutilized sites.
• Site managers in adapting communication and promotion strategies.
• Researchers in understanding digital tourism dynamics.

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Project Objectives

• Estimate the popularity of Senegalese tourist sites from social media images and metadata.
• Apply deep learning methods to classify and recognize tourist sites.
• Produce rankings and visualizations of site popularity.

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Data Sources

• Images collected from social media platforms (Instagram, Twitter, Facebook, etc.).
• Metadata: date, geolocation (if available), interactions (likes, shares, comments).
• Geographic reference data: official list of Senegalese tourist sites (for validation).

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Methodology

1. Data Collection – Retrieving images and metadata.
2. Preprocessing – Cleaning, filtering, and labeling images.
3. Learning / Modeling –
   • CNN with transfer learning (VGG16).
   • Potential comparison with other architectures (ResNet, EfficientNet, etc.).
4. Popularity Estimation – Aggregating results by site (frequency, user interactions).
5. Visualization – Graphs, maps, and rankings of the most popular sites.

Key repository files:

• CNN_transfert_learning_VGG16.py → CNN training with transfer learning.
• labelisation des images.ipynb → notebook for labeling images.

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Installation / Requirements

1. Clone the repository

git clone https://github.com/dalyo/Estimation-de-la-popularite-des-sites-touristiques-au-Senegal-a-partir-de-donnees-de-reseaux-sociaux.git
cd Estimation-de-la-popularite-des-sites-touristiques-au-Senegal-a-partir-de