T3C
Toxic Comment Classification Challenge
Spring 2018 - Data Preparation & Analysis Project
Group Members: Mridula Tunga (A20408428), Jose Luis Martin Casarubios (A20410665), Antoine Gargot (A20410860), Martin Berger (A20410866)
Subject: The goal of this project would be to take part in the Kaggle Challenge about Toxic Comment Classification.
Description (from Kaggle): Discussing things you care about can be difficult. The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions. Platforms struggle to effectively facilitate conversations, leading many communities to limit or completely shut down user comments.
The Conversation AI team, a research initiative founded by Jigsaw and Google (both a part of Alphabet) are working on tools to help improve online conversation. One area of focus is the study of negative online behaviors, like toxic comments (i.e. comments that are rude, disrespectful or otherwise likely to make someone leave a discussion). So far they’ve built a range of publicly available models served through the Perspective API, including toxicity. But the current models still make errors, and they don’t allow users to select which types of toxicity they’re interested in finding (e.g. some platforms may be fine with profanity, but not with other types of toxic content).
In this competition, you’re challenged to build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate better than Perspective’s current models. You’ll be using a dataset of comments from Wikipedia’s talk page edits. Improvements to the current model will hopefully help online discussion become more productive and respectful.
Disclaimer: the dataset for this competition contains text that may be considered profane, vulgar, or offensive.
Data Set : The data set consist in two cvs files. The first one containing the training sample and the second one the testing. They consist a large number of Wikipedia comments which have been labeled by human raters for toxic behavior. The types of toxicity are:
toxic severe_toxic obscene threat insult identity_hate
We must create a model which predicts a probability of each type of toxicity for each comment.
Link: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge