A streamlit app to analyze your whatsapp chats

Generate beautiful visual reports for your WhatsApp chats. Message stats, emoji analysis, calendar heatmaps, and more. Privacy-first: runs on your device or Google Colab.

An Exhaustive WhatsApp Chat Data Analysis.

Apply different analysis & data science tools on your Telegram / Whatsapp chat and get exciting insights!

Analysis of any chat in Whatsapp

performing sentiment analysis on the whatsapp chats.

Mine WhatsApp chat data and draw awesome inferences.

WhatsApp Chat Analyser uses your Whatsapp data and analysis it and extracts insides from your cart history.

A comprehensive Python library for analyzing WhatsApp chat exports. This library provides tools for preprocessing WhatsApp chat data and performing various analyses including sentiment analysis, user activity patterns, conversation patterns, and more.

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Whatsapp Chat Detective: A Streamlit application for Whatsapp Group Chats Analysis

Resagratia Whatsapp group chat analysis

WhatsAPp Group Chat Analysis Project

This project seeks to develop an interactive web app using Streamlit, which will display data analysis and visualization performed from either personal or group WhatsApp chats with Python.

Text & sentiment analysis of WhatsApp chat data.

Analysis of WhatsApp Group Chat

This repository seeks to contribute to the analysis of data in WhatsApp group chats in order to measure group activity and better lead an organization through data-driven decision making.

A next JS app that analysis your whatsapp chats and gives useful quirky insights

ChatInsight is a powerful web application designed to analyze WhatsApp chat data and provide detailed insights and visualizations. With an easy-to-use interface, ChatInsight allows users to upload their WhatsApp chat export files and instantly receive analysis results, including message counts, active days and times, commonly used words, and more.

This project seeks to perform data analysis in WhatsApp chats (individual and groups) using Python in order to measure group activity and better lead an organization through data-driven decision making.

This notebook was built to analyze Whatsapp conversations using the steps below: Step 1: Detecting {Date} and {Time} tokens Step 2: Detecting the {Author} token Step 3: Extracting and Combining tokens Step 4: Parsing the entire file and handling Multi-Line Messages For further steps, we need to perform Exploratory data analysis (EDA) Step 5: Performing EDA for analyzing chat data Step 6: Overall statistics of WhatsApp chat including Total number of messages, media messages(Omitted) & Total number of URLs Step 7: Extracting basic statistics for each Author (user) Step 8: Word cloud of most used words in chat Step 9: Total number of messages sent by each user Step 10: Total messages sent on each day of the week Step 11: Most active author of the chat Step 12: Most active day in a week In next steps, Time series analysis will be performed on chat data Step 13: Time whenever the chat was highly active Step 14: Date on which the chat was highly active Step 15: Converting 12-hour formate to 24 hours will help us for better analysis Step 16: Most suitable hour of the day whenever there will be more chances of getting a response from user

Interesting analysis and visualization that could be done on any WhatsApp chat

🤖 Whatsapp-Chat analysis This project provides an interactive dashboard to analyze WhatsApp chat data.

Sentiment Analysis on Whatsapp Chat

Visualize a whatsapp group using Dash-Plotly.

WhatsApp Chat Analysis WebApp

Check who is most active in your WhatsApp group using Python

WhatsApp has become one of the most trendy social media platform. WhatsApp Chat Analyzer is a platform that tracks our conversation and analyses group activities and how much time we are spending on WhatsApp.

Privacy‑first WhatsApp Chat Analyzer — drop your export to get instant, in‑browser stats & graphs.

The "WhatsApp Chats Analyzer" is a Python-based tool designed to provide detailed insights into your WhatsApp chat data. It offers features such as message statistics, participant analysis, timeline visualizations, emoji usage tracking, and word cloud generation, enabling users to comprehensively examine their conversations.