Using Sentiment Information for Preemptive Detection of Toxic Comments in Online Conversations
Table II: Prediction accuracy with and without sentiment features.
['Test', 'Features', 'Accuracy']
Our next experiment consists in training and testing our model with and without the sentiment features. The goal is to highlight the gain in prediction accuracy that comes from including sentiment features. In order to verify that theory, we re-trained and re-tested our model several times using between 1 and 7 positive or negative sentiment features. For toxicity prediction, nothing has changed, save for the fact the third negative word of the second message has disappeared (as the feature is no longer part of the model) and the second negative word of the second message becomes the seventh most predictive feature (it was eighth previously). This confirms our earlier intuition.