2. MAHATMA GANDHI MISSION’S COLLEGE OF
ENGINEERING AND TECHNOLOGY
COMPUTER SCIENCE ENGINEERING
ACADEMIC YEAR 2023-2024
SEMESTER VII
MAJOR PROJECT (10)
SUBJECT CODE : CSM701
3. Sr No. Name Of Students
Roll
No
UID
1 RITESH MISHRA 89 121CP1166A
2 VIBHANSHU DUBEY 32 120CP1198A
3 SUJIT RAJBHAR 125 120CP1206A
Group Members :
PROJECT GUIDE : PROF. RAJASHRI SONAWALE
5. Sr No. Topics
1. Abstract
2. Introduction
3. Problem Statement
4. Methodology
5. Literature Survey
6. Existing & Proposed System
7. Future Scope
8. Conclusion
Content Table :
6. Abstract
WhatsApp has been the most used mode of communication and has
been an efficient one too. It consists of many conversations in groups
and individuals. So, there might be some hidden facts in them. This
project takes those chats and provide a deep analysis of that data.
Being any topic, the chats are it provide the analysis in an efficient
and accurate way. The main advantage of this project is that it has
been built using libraries like pandas, seaborn, matplotlib, emoji etc.
They are used to create data frames and plot graphs in an efficient
way.
7. WhatsApp chat Analyzer is an analyzing tool for WhatsApp chats. The chat files can be
exported from WhatsApp and it generates various plots and graphs showing, the
number of messages or emojis, or images sent by a person, most active member in
the group etc. It helps us to have a better understanding of our WhatsApp chats. This
system is based on data analysis and pre-processing. The first step is pre-processing
and data preprocessing plays a major role when it comes to machine learning. In
order to apply the libraries, it has to be pre-processed and stored in an efficient way.
WhatsApp claims that nearly 55 billion messages are sent each day. The average user
spends 195 minutes per week on WhatsApp, and is a member of plenty of groups.
With this treasure house of data right under our very noses, it is imperative that we
embark on a mission to gain insights on the messages which our phones are forced
to bear witness to. A list that uses pie charts and diagrams to represent the
interesting data that it collects after analyzing your WhatsApp chats.
Introductio
n
8. Problem Statement
WhatsApp-Analyzer is a statistical analysis tool for WhatsApp chats. Working on the
chat files that can be exported from WhatsApp it generates various plots showing,
for example, which other participant a user responds to the most. Communication
between people using the internet becomes part of their daily life. People used to
communicate with each other using the online chat system to transfer their
messages. We propose to employ dataset manipulation techniques to have a
better understanding of WhatsApp chat present in our phones. It shows most used
emoji and word which repeatedly most times. It tracks our conversation and
analyzes how much time we are spending.
9. Title Authors
Technique
Used
Advantages Limitations
Whatsapp
Chat Analyzer
Ravishankara K,
Dhanush, Vaisakh,
April 2023
(IJERT)
StreamLit and
Machine
learning
The paper discusses a system
to detect anomalies in
WhatsApp conversations,
which is useful for identifying
unusual or suspicious
behavior
May have false positives and
false negatives in anomaly
detection.
WhatsApp Chat
Analysis for
Sentiment Detection
John Smith,
Emily Johnson,May-
2022
(IRJMETS)
Natural
Language
Processing
(NLP)
and
Machine
Learning
This paper presents a method
for sentiment analysis in
WhatsApp chats, which can
be used for understanding
user emotions and feedback.
Limited to analyzing
sentiment only, does not
handle multi-modal content.
Literature Survey
10. Existing System
●Chat Stats
●Whatsanalyze
●Chatilyzer
●Chat analyzer
• Technical Feasibility : The technical feasibility study reports whether there exists correct required
resources and technologies whichwill be used for project development. It is the measure of the
specific technical solution and the availability ofthe technical resources and expertise. In our project
we will be using Jupyter notebook(web based application)and VS code(text editor), both of them are
open source softwares.Along with these various python libraries andwill be used.
Operational Feasibility
It is to determine whether the system will be used after the development and implementation.In
Operational Feasibility degree of providing service to requirements is analyzed .This involves the
study of utilization and performance of the product. Our project shows the whole analysis of the
chats among people. It can be two people or a group of people and provides various information
using charts in easily readable format
12. Methodology
A. Data Analysis
It is a process of cleaning, transforming, inspecting and modelling data with the goal of discovering some useful
information and finally indicating some conclusions. Analysis means it breaks a whole component into its separate
components for individual ex amination. Data analysis is a process for acquiring raw data and transforming it into useful
information for decision-making by users. This project provides a basic statistical analysis WhatsApp chat. Following are
the an alysis made :
1.To find total messages, total words, total media and links shared in the WhatsApp
chat 2.To find the most active people in the group.
3.To find the most used emojis in the group.
4.To find the busiest day and least busy in a month.
5.To find the most frequently and commonly used words in the
group. 6.To find the frequency of chat in every day and month.
B. Proposed System
Data pre-processing is the initial part of the project, it is to understand the implementation and usage of various python
inbuilt m odules. These various modules provide better user understandability and code representation. The following
libraries are used such as NumPy, pandas, matplotlib, sys, re, emoji, seaborn etc. It analyses the data and gives top
statistics like total messages, total media, links, images shared, graphs showing the activity map weekly and monthly,
monthly timeline, daily timeline, mostly busy users, chart most common words used, emojis used.
13. C. Working
Steps to Export chat:
? Open WhatsApp chat for a group ->click on the menu ->click on more- ->select export chat->choose without
media. Working of WhatsApp chat analysis.
1.Intially open WhatsApp chat analyzer web
page. 2.Select Date format.
3.Upload the exported chat file.
4.Analyzing of data is done by trained model
5.Preprocessing of data is done by trained
model. 6.Select overall or single person
analysis
7.Trained model shows analysis it includes top statistics, word cloud, activity map, monthly timeline, daily timeline,
emoji analysis.
D. System Modules
5.Install and Import Dependencies: In this step Streamlit, matplotlib, pandas, collections, seaborn, emoji, Wordcloud,
URLextract, and re are installed and imported.
6.Pre-Processing: In this step pre-processing of the data is done. Here the data is formatted and separated in the
form of date, time, name of the user and message of the use.
7.Export chat Document from WhatsApp and Upload: Here the document is exported from WhatsApp. Steps to export
chat -
>Open individual or Group chat->Tap Options – More – Export Chat->Choose export without media-> Document
is set. Upload the chat file and click on analysis
4.Train Chat Model and Analyze the Data: Here the collected data is read and processed to train our machine
learning classification model on it. The model is then evaluated and serialized.
Methodology
14. Future Scope
• Security and Surveillance: Government organizations can benefit from improved chat analyzers
for monitoring and identifying potential security threats, criminal activities, and cyberattacks.
Advanced algorithms can be developed to automatically detect and report suspicious or illegal
content in chats.
• Data Visualization: Creating user-friendly dashboards and data visualization tools can help
government officials quickly grasp trends and insights from WhatsApp chat data, aiding
decision-making processes.
• Integration with Other Systems: Integrating WhatsApp chat analyzers with other government
systems, such as CRM, case management, and reporting tools, can provide a comprehensive view
of citizen interactions and issues, improving efficiency and accountability.
15. Conclusion
• We can conclude that the capabilities of the WhatsApp web application and
the power of the python programming language in implementing our data
analysis intended, cannot be overemphasized. The system was done with
python, and the python libraries that were implemented includes, StreamLit,
Emoji, NumPy, Pandas, Regular Expression, Matplotlib, URLextract, collection
and Seaborn. Finally results that we intended were obtained. The future of our
project is it is mainly useful for organization. Then will get to know who is more
and least active in the group. Depending on that they can take decisions.
16. References
1Ravishankara K, Dhanush, Vaisakh, Srajan I S, “International Journal of Engineering Research &
Technology (IJERT)”, ISSN: 2278-0181, Vol. 9 Issue 05, May-2020
2https://www.analyticsvidhya.com/blog/2021/06/build-web-app-instantly-for-machine-learningusing-
streamlit/
3 Meng Cai, “PubMed Central”, PMCID: PMC7944036, PMID: 33732917
4Dr. D. Lakshminarayanan, S. Prabhakaran, “Dogo Rangsang Research Journal”, UGC Care Group I
Journal, Vol-10 Issue-07 No. 12 July 2020
5 https://www.interaction-design.org/literature/topics/web-design
