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

Year : 2024 | Volume :11 | Issue : 02 | Page : –
By

M. Prasad,

Rajarao PBV,

P. Kiran Sree,

P. Gowthami,

K. Ajita Lakshmi,

B. Subrahmanyam,

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

Abstract

Nowadays everyone uses social media platforms like 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 SVM logistic regression nave bayes random forest decision tree and k-nearest neighbours KNN are systematically implemented 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 multi headed 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, SVM, KNN, Multi headed Model, Supervised Learning

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

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):-.
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):-. 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 May 3, 2024
Accepted May 9, 2024
Published July 5, 2024
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