A Supervised Learning Approach for Toxic Comment Detection on Social Media Platforms

Year : 2024 | Volume : 11 | Issue : 02 | Page : 7 14
    By

    M. Prasad,

  • Rajarao PBV,

  • P. Kiran Sree,

  • P. Gowthami,

  • K. Ajita Lakshmi,

  • B. Subrahmanyam,

  1. Associate Professor, Department of Computer Science Engineering, Shri Vishnu Engineering College for Women (A), Bhimavaram, Andhra Pradesh, India
  2. Associate Professor, Department of Computer Science Engineering, Shri Vishnu Engineering College for Women (A), Bhimavaram, Andhra Pradesh, India
  3. Professor and Head, Department of Computer Science Engineering, Shri Vishnu Engineering College for Women (A), Bhimavaram, Andhra Pradesh, India
  4. Student, Department of Computer Science Engineering, Shri Vishnu Engineering College for Women (A), Bhimavaram, Andhra Pradesh, India
  5. Assistant Professor, Department of Electronics and Communication Engineering, Shri Vishnu Engineering College for Women(A), Bhimavaram, Andhra Pradesh, India
  6. Assitant Professor, Department of MCA, BVC Institute of Technology and Science(A), Batlapalem, Andhra Pradesh, India

Abstract

Nowadays everyone uses social media platforms like X (formerly Twitter), Instagram, Facebook, etc. for various purposes. With the help of this, we share our opinions, ideas, and feelings. Generally, the datasets obtained from the internet are constructive; however, there is a significant proportion of toxic ones. The datasets are filtered to remove noise, and noise is removed in post-processing. The study initiates with the upload and preprocessing of a toxic comment dataset meticulously cleaning text by eliminating stop words and special symbols to lay out a standardized corpus. The resulting application of count vectorizer captures word occurrences constructing a feature matrix for algorithmic training supervised algorithms, including support vector machine, logistic regression, naive Bayes, random forest, decision tree, and k-nearest neighbors. These are systematically implemented and each algorithm undergoes rigorous assessment with accuracy measurements computed to check its proficiency in segregating toxic from non-toxic comments. The examination finishes in an accuracy graph visually contrasting the performance of the various supervised algorithms. This visual representation helps in identifying the most effective model for online toxic comment classification. The multiheaded model consists of toxicity, severe-toxic, obscene threat insults, and toxicity prediction based on confusion metrics. The practical implications of this study lie in outfitting a robust tool for online platforms to automatically detect and manage toxic comments adding to a safer and more constructive digital environment.

Keywords: Toxic comments, support vector machine (SVM), k-nearest neighbors (KNN), multiheaded model, supervised learning

[This article belongs to Journal of Mobile Computing, Communications & Mobile Networks ]

How to cite this article:
M. Prasad, Rajarao PBV, P. Kiran Sree, P. Gowthami, K. Ajita Lakshmi, B. Subrahmanyam. A Supervised Learning Approach for Toxic Comment Detection on Social Media Platforms. Journal of Mobile Computing, Communications & Mobile Networks. 2024; 11(02):7-14.
How to cite this URL:
M. Prasad, Rajarao PBV, P. Kiran Sree, P. Gowthami, K. Ajita Lakshmi, B. Subrahmanyam. A Supervised Learning Approach for Toxic Comment Detection on Social Media Platforms. Journal of Mobile Computing, Communications & Mobile Networks. 2024; 11(02):7-14. Available from: https://journals.stmjournals.com/jomccmn/article=2024/view=155358


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Regular Issue Subscription Review Article
Volume 11
Issue 02
Received 03/05/2024
Accepted 09/05/2024
Published 05/07/2024
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