Hate Speech Detection on Vietnamese Social Media Text using the Bidirectional-LSTM Model
This work addresses hate speech classification for Vietnamese social media, but it is incremental as it applies an existing method to a specific dataset.
The paper tackled hate speech detection on Vietnamese social media text by building a Bidirectional-LSTM model, achieving a result of 71.43% accuracy on the VLSP 2019 test set.
In this paper, we describe our system which participates in the shared task of Hate Speech Detection on Social Networks of VLSP 2019 evaluation campaign. We are provided with the pre-labeled dataset and an unlabeled dataset for social media comments or posts. Our mission is to pre-process and build machine learning models to classify comments/posts. In this report, we use Bidirectional Long Short-Term Memory to build the model that can predict labels for social media text according to Clean, Offensive, Hate. With this system, we achieve comparative results with 71.43% on the public standard test set of VLSP 2019.