Scholarlink

In the recent years, the WhatsApp app has been among the most effective for communication. Chats of numerous kinds that happen in a cluster of personnel and different kinds of subjects are included in WhatsApp chats. This application provides a detailed scrutiny of chats between users of a particular group and two individuals. Regardless of the topic on which the conversation is based, this web application can also be used to detect spam messages. This web application displays all messages, in the form of a graph every month, as well as the maximum used words and the active participant’s conversation. This web application also detects spam messages by a person and shows the total of spam messages in a particular chat. The benefit of this tool is that it has been developed using straightforward Python modules like Pandas, Streamline, Seaborn, and Word Cloud, as well as machine learning algorithms that create data frames and produce numerous graphs that are then displayed in the application. These algorithms are effective and resource-light, making them suitable for large datasets.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

WhatsApp Chat Analysis and Spam Discovery Using Machine Learning Models

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • A. Krishna,
  • Subhani Shaik,
  • G. Sreeja,
  • Bharat Kalyan,
  • Vijay Kumar

摘要

In the recent years, the WhatsApp app has been among the most effective for communication. Chats of numerous kinds that happen in a cluster of personnel and different kinds of subjects are included in WhatsApp chats. This application provides a detailed scrutiny of chats between users of a particular group and two individuals. Regardless of the topic on which the conversation is based, this web application can also be used to detect spam messages. This web application displays all messages, in the form of a graph every month, as well as the maximum used words and the active participant’s conversation. This web application also detects spam messages by a person and shows the total of spam messages in a particular chat. The benefit of this tool is that it has been developed using straightforward Python modules like Pandas, Streamline, Seaborn, and Word Cloud, as well as machine learning algorithms that create data frames and produce numerous graphs that are then displayed in the application. These algorithms are effective and resource-light, making them suitable for large datasets.