WhatsApp Group Chat Analysis Using Natural Language Processing (NLP)

Abstract

In the digital communication era, WhatsApp has
emerged as one of the most widely used
messaging platforms worldwide. With the
exponential growth of data shared through group
chats, analyzing this unstructured data using
advanced Natural Language Processing (NLP)
techniques has become essential for
understanding user behavior, communication
patterns, and group dynamics. This study
introduces an in-depth framework for WhatsApp
group chat analysis by leveraging NLP and
machine learning to extract meaningful insights
from exported chat logs.
The proposed system focuses on several key
objectives: identifying the most active and
inactive participants in a group, analyzing
message frequency over time, understanding
sentiment trends, and detecting frequently
discussed topics. The input to the system is the
raw text format of WhatsApp chats exported by
users. This data is then preprocessed using
various NLP methods including tokenization,
lemmatization, removal of stop words, and emoji
handling. Once cleaned, the dataset is subjected
to analytical processes such as frequency
analysis, word clouds, temporal message density
plots, and sentiment classification using libraries
like NLTK, TextBlob, and VADER.
In addition to basic chat statistics (such as the
number of messages, media files, links, and
deleted messages), our system performs sentiment
analysis to gauge the emotional tone of
conversations over time. This is particularly
useful in educational, corporate, or social
research settings where communication tone and
behavioral insights are important. Moreover,
topic modeling techniques such as Latent
Dirichlet Allocation (LDA) are used to extract
hidden themes in conversations, enabling a more
granular understanding of group discussions.
The system also introduces a visual dashboard
that presents key findings in the form of graphs,
heatmaps, and pie charts. For example, daily or
weekly activity trends are visualized to show peak
interaction times, while pie charts display the
proportional contribution of each participant.
Deleted message tracking helps identify possible
sensitive or hidden content trends, which may be
important in digital forensics or behavior
monitoring.
Through real-world datasets collected from
multiple anonymous WhatsApp groups
(educational, work-related, and casual), the
analysis demonstrated consistent accuracy in
detecting message patterns, identifying leading
contributors, and mapping emotional tone
changes over time. These insights are not only
beneficial for sociologists and digital
communication researchers but also applicable in
business, education, and legal domains for
analyzing team dynamics, compliance, and
engagement.
This research contributes to the field of text
analytics by demonstrating how powerful insights
can be extracted from personal and group chat
data using NLP. It also opens doors for future
enhancements such as real-time chat analysis,
multilingual sentiment evaluation, spam
detection, and integration with advanced AI
models like transformers and LLMs for deeper
conversational understanding.
In conclusion, this WhatsApp Group Analysis
system transforms static chat logs into dynamic
and interactive interpretations of digital
conversations. It bridges the gap between raw
data and decision-making, providing a tool for
both academic exploration and practical
applications in the modern communication
landscape.