Toxic Comment Classification

The purpose of this project is to perform binary classification on natural language to determine if it is safe or unsafe in nature.

Data Processing

This project uses a combination of two Kaggle competition datasets, namely:

1. Jigsaw Toxic Comment Classification Challenge
2. Jigsaw Unintended Bias in Toxicity Classification

In total these datasets consisted of around 2 million internet comments from a variety of sources, with around 10% of this data classified as unsafe. The two datasets were cleaned, and recombined and then balanced out to form a new unbiased dataset, which consists of around 360,000 comments with a 50/50 balance between safe and unsafe classes.

Classification

Four different models were experimented with in this project

• Random Forests
• Logistic Regression
• Multilayer Perceptron
• Deep Learning

The results of these techniques were then compared. Additionally a few natural language processing (NLP) techniques were applied to some of the models. These NLP techniques consisted of the following,

• Alphanumeric tokens only
• Stopword removal
• Nouns only

Results

The following table summarizes the results achieved by the best models from each model category.

Method	Accuracy	Precision	Recall
Random Forests	0.854074	0.854281	0.854085
Logistic Regression	0.870507	0.871464	0.870392
Deep Learning	0.890919	0.891543	0.891011