Abstract
Hate speech is when someone or a group of people is insulted or stigmatized based on their background, colour, gender, religion, or other traits. Additionally, social media generates massive amounts of data every day. However, due to data peculiarities, a single classifier cannot deliver the heterogeneous feature for text classification. As a result, a novel fusion RNN (BiLSTM-BiGRU)-Multichannel CNN-Capsule Network-Attention (RMCCA) is presented in this research. The proposed approach improves in classification improvement. By eliminating ambiguity and text granularities, the suggested method facilitates in strengthening classification accuracy and ground truth evidence. Separate data sets are used to validate the suggested models. The empirical results show that the offered methods produce sufficient hate speech classification results.
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Shah, P., Patel, A. (2022). A Novel Multimodal Fusion Technique for Text Based Hate Speech Classification. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1614. Springer, Cham. https://doi.org/10.1007/978-3-031-12641-3_29
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