Leveraging Sentiment for Offensive Text Classification
This work addresses offensive text detection, an important task for online safety, but it is incremental as it applies an existing method to a specific dataset.
The paper investigated whether incorporating sentiment analysis improves offensive text classification, finding that using sentiment increased model performance on the OLID dataset.
In this paper, we conduct experiment to analyze whether models can classify offensive texts better with the help of sentiment. We conduct this experiment on the SemEval 2019 task 6, OLID, dataset. First, we utilize pre-trained language models to predict the sentiment of each instance. Later we pick the model that achieved the best performance on the OLID test set, and train it on the augmented OLID set to analyze the performance. Results show that utilizing sentiment increases the overall performance of the model.