WhatsApp Chat Analysis Using Machine Learning Approach-Project-Review.
Introduction:
A WhatsApp Chat Analyzer is a tool or software application that is designed to analyze and extract data from WhatsApp chat histories.
The tool can be used to visualize and understand patterns and trends in chat conversations, such as the frequency of messages, the most active users, and the use of specific words or phrases.
The WhatsApp Chat Analyzer can be used for a variety of purposes, including personal data analysis, business intelligence, and customer support.
It can help individuals and organizations gain insights into the communication patterns and behaviors of their chat participants, and can be useful for identifying trends, analyzing customer sentiment, and improving communication strategies.
The WhatsApp Chat Analyzer typically involves the extraction and processing of data from WhatsApp chat logs, which are saved in the form of text or CSV files.
The tool may also include features for data visualization, such as graphs and charts, as well as analysis tools for identifying patterns and trends in the data. Some WhatsApp Chat Analyzers may also include features for data export, allowing users to share their analysis results with others.
Problem Statement:
If we talk about the Social communication medium, the WhatsApp comes at top and it is difficult for individual and groups to gain deeper insights into communication patterns and trends and users may have difficulty to understanding how they communicate on WhatsApp and how their messaging habits have changed over time.
They may also struggle to identify any trends or patterns in their chat history, such as the most frequently used words or the most active users, although WhatsApp provides basic statistics about a user's chat history, such as the number of messages sent and received.
The proposed system namely “WhatsApp Chat Analyzer using machine leaning approach ” addresses this problem by providing a tool that allows users to import and parse their WhatsApp chat history.
Project Objectives:
The objectives are outlined below:
Ensures to provide an in-depth exploratory data analysis on various types of WhatsApp chats.
To provide a tool that allows users to import and analyze their WhatsApp chat history.
To extract relevant information from the chat history, such as the number of messages sent, the most active users, and the most frequently used words.
To visualize the chat data using graphs and charts, making it easier for users to understand and analyze their communication patterns and trends.
Scope and Motivation:
The WhatsApp Chat Analyzer can help users identify trends and changes in their messaging habits and make more informed decisions about their communication.
The WhatsApp Chat Analyzer can also be useful for researchers and analysts who want to study communication patterns and trends on WhatsApp.
The tool can facilitate the research process and provide valuable insights for academic and commercial purposes.
Related Work:
Chat Mapper: Analyze and understand their customer conversations.
Chat Analyzer: Allowing businesses to analyze customer conversations across multiple channels. Facebook, Twitter, Instagram etc.
Many Chat: Chabot platform, helps businesses to create and manage Chabot's for messaging social platforms.
Chat analytics: It provides features like sentiment analysis, conversation flow analysis, and customer segmentation.
Methodology / Plan of work:
Prototyping model
Proposed Model
Use case diagrams
Prototype of proposed system:
Proposed Model:
Use case Diagram:
Tools & Techniques:
Pycharm IDE:-
PyCharm is a hybrid platform developed by JetBrains as an IDE for Python. It is commonly used for Python application development. Some of the unicorn organizations such as Twitter, Facebook, Amazon, and Pinterest use PyCharm as their Python IDE.
Jupyter Notebook:-
The Jupyter Notebook is an open source web application that you can use to create and share documents that contain live code, equations, visualizations, and text. Jupyter Notebook is maintained by the people at Project Jupyter,
Python:-
It is a general-purpose programming language. It provides different types of libraries which provides different functionality to project. Python is used for predictions and pattern using test and data. Python consist many libraries which provide mathematical, statistical functions and help to find insights from data.
Pandas:-
This is an open-source Python library which is mainly used in Data Science and machine learning subjects. This library provides analysis tool for data manipulation, using its data structures this are used for analyzing data for manipulating time series analysis and numerical data.
RE:-
A regular expression is a special sequence of characters that helps you match or find other strings or sets of strings, using a specialized syntax held in a pattern. Regular expressions are widely used in UNIX world.
Matplotlib:-
Matplotlib is easy to use and an amazing visualizing library in Python. It is built on Numpy arrays and it work with the broader SciPy stack and consists of several plots like pie, line, bar, graph, scatter, histogram, etc. In this project, Matplotlib is used for various visualizations for analysis of WhatsApp chats. Visualizations like bar charts, line charts, pie charts are used.
Seaborn:-
Seaborn is a library mostly used for statistical plotting in Python. To make statistical plots more attractive it provides beautiful color palettes and default styles. In this project, Seaborn is used for heat map visualization for showing 24 hours with 7 days with different scale of color for getting hour with max to min messages.
Streamlit:-
In this project, this library is used for creating beautiful web items and objects for representing WhatsApp chat analysis with different types of charts and visualizations on Streamlit. [7]
NLP:-
In this project, Features of NLP are used like Parsing Text, eliminating stop words and Analyzing Text. Parsing text is used for splitting messages into words for analysis like total words and mostly used words. A file is used that contains all stop words which is given to the python program to show meaningful words only by eliminating all stop words. Text analysis is used to identify how many media are shared, how many links are shared. [8]
Emoji:-
Emoji Module is a Python package that allows us to use and print emoji's through a Python program, and we can even use this module to use emoji's inside an application we are creating using Python. [9]
Results and Discussions:
We have successfully designed and implemented the “WhatsApp Chat Analyzer” that allows users to import and analyze their WhatsApp chat history.
The proposed system has been implemented and tested on different 40 WhatsApp groups of different domains such as, educations, business, marketing, ecommerce.
We got some suggestions from users like the Show the total messages, total words, Media shared, & total links shared, and User-friendly, by users, then we modified our system according to the feedback.
Conclusion:
The proposed project has been developed and implemented successfully.
A WhatsApp Chat Analyzer is a tool or software application that is designed to analyze and extract data from WhatsApp chat histories, which is implemented and tested on different 40 WhatsApp groups of different domains such as, educations, business, marketing, ecommerce and provided positive usability testing.
The WhatsApp Chat Analyzer allow the users to analyze and visualize their WhatsApp chat history. The tool should be able to import and parse chat history from a text file, extract relevant information, and present the data in a visually appealing and easy-to-understand manner.
This project shows how present development may shape the future of online WhatsApp chat analyzer by adopting the new tends in the technology revolution.
Future Work:
As research never ends, this work can be extended for further deployment on more domain WhatsApp chat groups.
Furthermore sentiment analysis will be applied in order to analysis behavior of group members.
The proposed system can be extended for various social media platforms.
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