Snapchat Data Extraction & Analysis

A Python toolkit for extracting and analyzing Snapchat data exports. This project processes raw Snapchat data (JSON/HTML) from multiple users and generates comprehensive cohort-level analytics.

Project Structure

SnapAnalysis_Extraction/
├── Snapchat_Data/
│   ├── Snapchat_donated_data/   # (Optional) Raw zip files
│   └── Extracted_Users/         # Extracted user folders (required)
│       ├── user_id_1/
│       │   └── html/
│       └── user_id_2/
│           └── html/
├── extracted_csvs/              # Output: processed CSV files
├── analysis_plots/              # Output: generated visualizations
├── extract_snapchat_data.py
├── analyze_snapchat_data.py
├── requirements.txt
└── README.md

Quick Start

1. Setup Environment

# Create conda environment
conda create -n snap_env python=3.10 -y
conda activate snap_env

# Install dependencies
pip install -r requirements.txt

2. Prepare Data

Place your Snapchat data exports in Snapchat_Data/Extracted_Users/. Each user should have their own directory containing html/ and optionally json/ folders.

3. Extract Data

python extract_snapchat_data.py

This parses all user data and generates CSV files in extracted_csvs/:

• chats.csv - Chat message metadata
• friends.csv - Friend connections
• memories.csv - Saved memories/snaps
• myai.csv - My AI interactions
• snap_history_log.csv - Snap send/receive history
• talk_history_*.csv - Call logs

4. Run Analysis

python analyze_snapchat_data.py

This generates:

• analysis_report.md - Comprehensive cohort analysis
• analysis_plots/ - Visualizations (trends, distributions, wordclouds)

Output Files

Extracted CSVs

File	Description
chats.csv	Message metadata (sender, timestamp, type)
friends.csv	Friend list with timestamps
memories.csv	Saved snaps with location/media type
myai.csv	My AI conversation logs
snap_history_log.csv	Snap send/receive history

Analysis Visualizations

Plot	Description
global_yearly_trend.png	Total messages per year
average_user_yearly_trend.png	Mean messages/user per year
seasonality_trend.png	Activity by month of year
global_hourly_trend.png	Activity by hour of day
global_weekly_trend.png	Activity by day of week
msg_distribution.png	Distribution of messages per user
friend_distribution.png	Distribution of friends per user
conversation_wordcloud.png	Common words in conversations
myai_wordcloud.png	Common words in My AI responses

Requirements

• Python 3.10+
• pandas, matplotlib, seaborn, wordcloud, beautifulsoup4, tqdm

License

For educational/research purposes only.