Using Sentiment Information for Preemptive Detection of Toxic Comments in Online Conversations
Table III: Results for both models using text features alone (190 features) or text and sentiment features (260 features).
['Model', 'Features', 'Accuracy', 'F1 score']
The data was randomly split 70/20/10 into training/validation/testing sets. We once again did 10 training and testing runs, using a different random split each time and 5-fold cross-validation within each run. These results confirm that adding sentiment information helps improve the prediction of toxic conversations. The gain is greater for the logistic regression model, which in fact fails to make a prediction better than random chance without sentiment information. The RNN fares better, probably because it can better handle the large number and sequential nature of the features, but it still gains 1% by including sentiment information.