Scholarlink

The identification of offensive content encounters difficulties in subtlety and context, requiring the use of advanced preprocessing and feature extraction techniques as well as ongoing refinement to effectively navigate the virtual communication landscapes. To combat offensive content detection in virtual environments, a novel system is proposed that integrates deep learning, machine learning, and natural language processing. The research adopts sophisticated preprocessing techniques and feature extraction to uncover the subtleties of different models through the exploration of a variety of datasets. A distinct contribution is highlighted through the introduction of a predictive analytics framework along with a user-blocking system to identify individuals engaging in posting offensive and hateful comments.

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

Unveiling Offensive Content Detection: An Experimental Study Using Deep Learning and Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Ananya Gupta,
  • Akashvi Bhardwaj,
  • Harshita Sharma,
  • Sonakshi Vij

摘要

The identification of offensive content encounters difficulties in subtlety and context, requiring the use of advanced preprocessing and feature extraction techniques as well as ongoing refinement to effectively navigate the virtual communication landscapes. To combat offensive content detection in virtual environments, a novel system is proposed that integrates deep learning, machine learning, and natural language processing. The research adopts sophisticated preprocessing techniques and feature extraction to uncover the subtleties of different models through the exploration of a variety of datasets. A distinct contribution is highlighted through the introduction of a predictive analytics framework along with a user-blocking system to identify individuals engaging in posting offensive and hateful comments.