WhatsApp Chat Analyzer

Project Definition: The WhatsApp Chat Analyzer is a tool designed to analyse and extract insights from WhatsApp chat data. The primary goal is to provide users with a comprehensive overview of their conversation patterns, including message frequency, media sharing, sentiment analysis, and more. By processing exported chat data, the tool aims to offer meaningful visualizations and statistics to enhance users' understanding of their communication habits.

Features:

1. Basic Statistics: • Total number of messages. • Count of media shared. • Number of links shared. • Average sentiment analysis of messages.
2. Timeline Analysis: • Monthly timeline visualization. • Weekly timeline visualization. • Daily timeline visualization.
3. Activity Map: • Most active month analysis. • Most active day analysis.
4. User Engagement: • Identification of the most active users. • Percentage of messages contributed by each user.
5. Word Analysis: • Word cloud for common words. • Word frequency analysis.
6. Sentiment Analysis: • Histogram depicting sentiment polarity distribution.
7. Chat Growth Analysis: • Cumulative chat growth over time for all users.
8. Reply Time Analysis: • Statistics on reply times for each user. • Identification of the fastest replier.
9. Mood Swing Chart: • Visualization of overall mood swing with total messages and media shared.

Workflow of This Project:

1. Data Preprocessing: • Extraction of messages, dates, and times using regular expressions. • Creation of a structured DataFrame with user, message, date, time, and additional information.
2. Data Analysis Functions: • Implementation of functions for statistical analysis, timeline generation, activity mapping, and more. • Integration of sentiment analysis and word frequency calculations.
3. Streamlit Interface: • Creation of a user-friendly interface using Streamlit. • File upload functionality for WhatsApp chat data. • Dropdown menu for user selection and analysis trigger button.
4. Visualization: • Utilization of Matplotlib and Seaborn for creating visualizations. • Display of various charts, timelines, and word clouds.
5. User Interaction: • Dynamic updates based on user selections (e.g., selected user for analysis). • Detailed information provided upon analysis button click.

Tools and Technologies: • Programming Language: Python • Libraries and Frameworks: pandas, re, urlextract, TextBlob, WordCloud, Matplotlib, Seaborn, Streamlit.

Use Case Area: The WhatsApp Chat Analyzer is beneficial for individuals and groups who want to gain insights into their communication patterns. It can be particularly useful for: • Understanding user engagement and participation. • Monitoring sentiment trends over time. • Analysing peak activity months and days. • Identifying the most active users in the chat. • Exploring common words and word frequencies.

Conclusion: The WhatsApp Chat Analyzer project offers a valuable solution for users seeking a deeper understanding of their WhatsApp conversations. By leveraging data analysis and visualization techniques, the tool provides a comprehensive overview of chat dynamics, enabling users to draw meaningful insights from their communication patterns. The interactive interface, coupled with a range of analytical features, makes it a versatile and user-friendly tool for anyone interested in exploring and understanding their WhatsApp chats.