WhatsApp Chat Analyzer Using Python and Streamlit

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WhatsApp Chat Analyzer Using Python and Streamlit

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Detail Description

1. Abstract

With the increasing use of social media applications, people communicate daily through messaging platforms such as WhatsApp. These chats contain valuable information that can be analyzed to understand communication patterns, user activity, and group behavior.

This project focuses on developing a WhatsApp Chat Analyzer that analyzes personal and group chat data. The system takes exported chat files as input and extracts useful features such as total messages, word count, media shared, emojis used, and active users. The extracted data is then visualized using charts and graphs.

The application is developed using Python and Streamlit for the user interface. It provides an interactive platform where users can upload chat files and view detailed analysis. The system can be deployed on cloud platforms such as Heroku for easy access.

2. Objectives

The main objectives of this project are:

To understand text data analysis techniques.
To learn how to preprocess chat data.
To extract useful features from chat files.
To analyze group and personal conversations.
To generate statistical insights.
To visualize data using graphs and charts.
To create an interactive user interface.
To integrate analysis with Streamlit.
To deploy the application on cloud platform
To improve data analysis skills.

3. Existing System

In the existing system, users can only view WhatsApp chats manually without any analytical tools.

The limitations of the existing system are:

No automated analysis.
Manual reading of chats is time-consuming.
No statistical summaries.
No visualization support.
Difficult to track active users.
No word usage analysis.
No emoji usage insights.

These limitations make it difficult to understand chat behavior effectively.

4. Proposed System

The proposed system uses Python and data analysis techniques to analyze WhatsApp chat data automatically.

In this system:

• Users upload chat text files.

• Chat data is cleaned and preprocessed.

• Features are extracted from messages.

• Statistical analysis is performed.

• Graphs and charts are generated.

• Word cloud is created.

• Emoji analysis is done.

• Streamlit interface is used.

• Application is deployed on cloud.This system provides accurate and interactive chat analysis.

5. Implementation Procedure

The project is implemented using the following steps:

Step 1: Data Collection

Export WhatsApp chat without media and save as text file.

Step 2: Data Upload

Upload the chat file using Streamlit interface.

Step 3: Data Preprocessing

• Remove unnecessary symbols

• Clean timestamps

• Format messages

• Handle missing values

Step 4: Feature Extraction

Extract features such as:

• Total messages

• Total words

• Media count

• Links shared

• Emoji usage

Step 5: Data Analysis

Perform statistical analysis on extracted features.

Step 6: Visualization

Generate charts, bar graphs, timelines, and word clouds.

Step 7: User Selection

Allow user-wise and overall chat analysis.

Step 8: Deployment

Deploy the application on Heroku or cloud server.

6. Software Requirements

The software tools required for this project are:

• Python

• Jupyter Notebook / VS Code

• Pandas, NumPy

• Matplotlib, Seaborn

• WordCloud Library

• Emoji Library

• Streamlit

• Heroku

• Web Browser

7. Hardware Requirements

The hardware requirements include:

• Processor: Intel i3 or higher

• RAM: 4 GB or higher

• Storage: 128 GB or higher

• System: Laptop/Desktop

• Internet Connection: Stable broadband

Optional:

• Cloud server for hosting

8. Advantages of the Project

Provides detailed chat insights.
Saves time in manual analysis.
Easy to use interface.
Supports group and personal chats.
Displays visual reports.
Improves understanding of communication patterns.
Useful for students and researchers.
Scalable for large chat files.
Helps track active users.
Enhances data analytics skills.

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