Combining FastText and Glove Word Embedding for Offensive and Hate speech Text Detection
Nabil Badri, Férihane Kboubi, Anja Habacha Chaïbi
Abstract
Over the past decade, increased use of social media has led to an increase in hate content. To address this issue new solutions must be implemented to filter out this kind of inappropriate content. Because manual filtering is difficult, several studies have been conducted in order to automate the process. This paper introduces a method based on a combination of Glove and FastText word embedding as input features and a BiGRU model to identify hate speech from social media websites. The obtained results show that our proposed model (BiGRU Glove FT) is effective in detecting inappropriate content. This model detect hate speech on OLID dataset, using an effective learning process that classifies the text into offensive and not offensive language. The performance of the system attained 84%, 87%, 93%, 90% accuracy, precision, recall, and f1-score respectively.