Description

The hate speech problem on online social networks (OSNs) is also widespread. The existing literature has machine learning approaches for hate speech detection on OSNs. However, the effectiveness of contextual information at different orientations is understudied. This study presents a novel Convolutional, BiGRU, and Capsule network-based deep learning model, HCovBi-Caps, to classify the hate speech. The proposed model is evaluated over two Twitter-based benchmark datasets – DS1(balanced) and DS2(unbalanced) with the best performance of 0.90, 0.80, and 0.84 respectively considering precision, recall, and f-score over unbalanced dataset. In terms of training and validation accuracy, the proposed model shows the best performance of 0.93 and 0.90, respectively, over the unbalanced dataset There have been improvements in ML algorithms that were employed for hate speech detection over time. In this process, this is going to find or detecting the speech recognition such as hate word, offensive word, normal word are going to be the class variable of our dataset classes. In this approach, going to implement the machine learning algorithms to analyze the process to find the accuracy, precision and f1-score.