A deep learning ensemble hate speech detection approach for sinhala tweets

Abstract

We live in an era where social media platforms play a key role in society. These platforms support most of the native languages and this has enabled people to express their opinions conveniently. Also, it is very common to observe that people express very hateful opinions on social media platforms as well. Several studies have been carried out in this area for the Sinhala language with traditional machine learning models and none of them have shown promising results. Further, current approaches are far behind the latest techniques carried out in high-resource languages. Hence this study presents a deep learning-based approach for hate speech detection which has shown outstanding results for other languages. Moreover, a deep learning ensemble was constructed from these models to evaluate performance improvements. These models were trained and tested on a newly created dataset using the Twitter API. Moreover, the model generalizability was further tested by applying it to a completely new dataset. As per the results, it can be observed that the proposing approach has outperformed the traditional machine learning models and is well generalized. Finally, the experimentation with extra features also reveals that there is a positive impact on the performance using extra features.