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WhatsApp group chats generate large amounts of conversational data that can be analysed for valuable insights. This project focuses on studying user interactions, frequently discussed topics, and sentiment trends in WhatsApp group conversations using data science and machine learning techniques. A publicly available dataset from Kaggle will serve as the foundation for this study. Data preprocessing will be carried out using Pandas, while Matplotlib and Seaborn will assist in visualization. Natural Language Processing (NLP) techniques, implemented via NLTK, will refine the text through tokenization and stop word removal. TF-IDF will identify important keywords, and VADER will classify messages based on sentiment—positive, negative, or neutral. Several analytical methods will be used, including word frequency analysis, sentiment analysis, and message length distribution. Regular expressions will extract timestamps, sender details, and messages for structured analysis. To identify key discussion themes, K-Means clustering will categorize similar messages. Additionally, network analysis will help pinpoint the most active participants in the group. By leveraging Python-based tools and machine learning techniques, this system aims to efficiently process and interpret WhatsApp chat data. The approach ensures scalability, making it applicable to diverse group conversations, ultimately providing deeper insights into communication patterns.

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Performance of NLP Techniques for Data Analysis of Social Media Application

  • 会议论文

  • 发表日期：

    2026年

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  • Ch Dileep Chakravarthy,
  • I. Hemalatha,
  • K. Pavan Lokesh,
  • O. Pramod Krishna,
  • N. Hitesh,
  • M. G. A. S. G. Yaswanth

摘要

WhatsApp group chats generate large amounts of conversational data that can be analysed for valuable insights. This project focuses on studying user interactions, frequently discussed topics, and sentiment trends in WhatsApp group conversations using data science and machine learning techniques. A publicly available dataset from Kaggle will serve as the foundation for this study. Data preprocessing will be carried out using Pandas, while Matplotlib and Seaborn will assist in visualization. Natural Language Processing (NLP) techniques, implemented via NLTK, will refine the text through tokenization and stop word removal. TF-IDF will identify important keywords, and VADER will classify messages based on sentiment—positive, negative, or neutral. Several analytical methods will be used, including word frequency analysis, sentiment analysis, and message length distribution. Regular expressions will extract timestamps, sender details, and messages for structured analysis. To identify key discussion themes, K-Means clustering will categorize similar messages. Additionally, network analysis will help pinpoint the most active participants in the group. By leveraging Python-based tools and machine learning techniques, this system aims to efficiently process and interpret WhatsApp chat data. The approach ensures scalability, making it applicable to diverse group conversations, ultimately providing deeper insights into communication patterns.