A Machine Learning Approach to Comment Toxicity Classification

02/27/2019 ∙ by Navoneel Chakrabarty, et al. ∙ 0

Now-a-days, derogatory comments are often made by one another, not only in offline environment but also immensely in online environments like social networking websites and online communities. So, an Identification combined with Prevention System in all social networking websites and applications, including all the communities, existing in the digital world is a necessity. In such a system, the Identification Block should identify any negative online behaviour and should signal the Prevention Block to take action accordingly. This study aims to analyse any piece of text and detecting different types of toxicity like obscenity, threats, insults and identity-based hatred. The labelled Wikipedia Comment Dataset prepared by Jigsaw is used for the purpose. A 6-headed Machine Learning tf-idf Model has been made and trained separately, yielding a Mean Validation Accuracy of 98.08 of 91.61 online conversation

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 9

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.