WhatsApp Group Chat Analysis Using Natural Language Processing (NLP)

In the digital communication era, WhatsApp hasemerged as one of the most widely usedmessaging platforms worldwide. With theexponential growth of data shared through groupchats, analyzing this unstructured data usingadvanced Natural Language Processing (NLP)techniques has become essential forunderstanding user behavior, communicationpatterns, and group dynamics. This studyintroduces an in-depth framework for WhatsAppgroup chat analysis by leveraging NLP andmachine learning to extract meaningful insightsfrom exported chat logs.The proposed system focuses on several keyobjectives: identifying the most active andinactive participants in a group, analyzingmessage frequency over time, understandingsentiment trends, and detecting frequentlydiscussed topics. The input to the system is theraw text format of WhatsApp chats exported byusers. This data is then preprocessed usingvarious NLP methods including tokenization,lemmatization, removal of stop words, and emojihandling. Once cleaned, the dataset is subjectedto analytical processes such as frequencyanalysis, word clouds, temporal message densityplots, and sentiment classification using librarieslike NLTK, TextBlob, and VADER.In addition to basic chat statistics (such as thenumber of messages, media files, links, anddeleted messages), our system performs sentimentanalysis to gauge the emotional tone ofconversations over time. This is particularlyuseful in educational, corporate, or socialresearch settings where communication tone andbehavioral insights are important. Moreover,topic modeling techniques such as LatentDirichlet Allocation (LDA) are used to extracthidden themes in conversations, enabling a moregranular understanding of group discussions.The system also introduces a visual dashboardthat presents key findings in the form of graphs,heatmaps, and pie charts. For example, daily orweekly activity trends are visualized to show peakinteraction times, while pie charts display theproportional contribution of each participant.Deleted message tracking helps identify possiblesensitive or hidden content trends, which may beimportant in digital forensics or behaviormonitoring.Through real-world datasets collected frommultiple anonymous WhatsApp groups(educational, work-related, and casual), theanalysis demonstrated consistent accuracy indetecting message patterns, identifying leadingcontributors, and mapping emotional tonechanges over time. These insights are not onlybeneficial for sociologists and digitalcommunication researchers but also applicable inbusiness, education, and legal domains foranalyzing team dynamics, compliance, andengagement.This research contributes to the field of textanalytics by demonstrating how powerful insightscan be extracted from personal and group chatdata using NLP. It also opens doors for futureenhancements such as real-time chat analysis,multilingual sentiment evaluation, spamdetection, and integration with advanced AImodels like transformers and LLMs for deeperconversational understanding.In conclusion, this WhatsApp Group Analysissystem transforms static chat logs into dynamicand interactive interpretations of digitalconversations. It bridges the gap between rawdata and decision-making, providing a tool forboth academic exploration and practicalapplications in the modern communicationlandscape.