📊 WhatsApp Chat Analyzer Dashboard

A powerful Streamlit-based dashboard to analyze WhatsApp chat exports — uncovering communication trends, sentiment insights, keyword usage, relationship dynamics, and more!

This project includes automated chat parsing + an interactive data analytics dashboard. It supports word clouds, reply-time analysis, conversation flow, temporal behavior, and even basic ML-based insights.

---

🚀 Features

✔ WhatsApp Chat → Structured Data

Convert raw .txt chat exports into CSV/Excel format using the script

✔ Analytics & Visualizations Dashboard

Built with Streamlit, offering:

Module	Insights
Overview	Total messages, active days, chat highlights
Basic Stats	Who texts more? Message length distribution
Keyword Analysis	Common phrases usage count
Temporal Patterns	Hourly + weekday communication patterns
Conversation Flow	Reply-time analysis, conversation starters
Word Clouds	Personalized clouds for each participant
Awards & Metrics	Fun badges like Chatterbox & Most Romantic
ML Predictions	Love message probability (demo model)
AI Chat (Cohere)	Ask AI questions about your chat insights

Dashboard source:

---

🏗 Tech Stack

Category	Tools
Frontend UI	Streamlit
Data Processing	pandas, numpy
Visualization	matplotlib, seaborn, wordcloud
Sentiment Analysis	TextBlob
ML Models	scikit-learn
Optional AI Querying	Cohere

All dependencies:

---

📥 Installation & Setup

1️⃣ Clone repo

git clone https://github.com/yourusername/whatsapp-chat-analyzer.git
cd whatsapp-chat-analyzer

2️⃣ Install dependencies

pip install -r requirements.txt

---

🔄 Convert WhatsApp Chat File

Export your WhatsApp chat as .txt, name it WhatsApp.txt, then run:

python "WhatsappChat to csv.py"

This will generate:

cleaned_messages.csv
cleaned_messages.xlsx

---

▶ Run the Dashboard

Inside the project folder:

streamlit run "ChatAnalyzer.py"

Once launched, upload/ensure cleaned_messages.csv is available — analytics begin instantly!

---

🔐 Optional: AI Analysis Setup

To use Cohere-powered AI insights:

1. Create an API key → https://dashboard.cohere.com
2. Add it to your Streamlit secrets:

.streamlit/secrets.toml

cohere_api_key="YOUR_API_KEY"

Or edit directly in the script.

---

📈 Example Insights You’ll Get

• Who initiates conversations more?
• Emotional positivity vs negativity over time
• Most active communication hours & days
• Keyword heatmap (Good morning / Sorry / Love etc.)
• Reply behavior — faster responder?
• Personalized word clouds
• Fun couple analytics 🥰

---

📌 Future Enhancements (Planned)

• More robust chat parser supporting multiple date formats
• Topic clustering using NLP
• Advanced sentiment trend forecasting
• Multi-chat comparison

---

👩‍💻 Author

Love Mathur Data Analyst & ML Developer

---