Bangla hate speech detection on social media using attention-based recurrent neural network
| Autor: | Anik Paul, Abdullah Al Asif, Md. Nur Hossain, Amit Kumar Das |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Science - Artificial Intelligence Speech recognition Science 02 engineering and technology lstm Machine Learning (cs.LG) bangla hate speech detection 03 medical and health sciences Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering Social media bangla text classification 030505 public health Voice activity detection Computer Science - Computation and Language gru QA75.5-76.95 rnn language.human_language Artificial Intelligence (cs.AI) Recurrent neural network Bengali Electronic computers. Computer science language 0305 other medical science attention mechanism Computation and Language (cs.CL) Software Information Systems |
| Zdroj: | Journal of Intelligent Systems, Vol 30, Iss 1, Pp 578-591 (2021) |
| Popis: | Hate speech has spread more rapidly through the daily use of technology and, most notably, by sharing your opinions or feelings on social media in a negative aspect. Although numerous works have been carried out in detecting hate speeches in English, German, and other languages, very few works have been carried out in the context of the Bengali language. In contrast, millions of people communicate on social media in Bengali. The few existing works that have been carried out need improvements in both accuracy and interpretability. This article proposed encoder–decoder-based machine learning model, a popular tool in NLP, to classify user’s Bengali comments from Facebook pages. A dataset of 7,425 Bengali comments, consisting of seven distinct categories of hate speeches, was used to train and evaluate our model. For extracting and encoding local features from the comments, 1D convolutional layers were used. Finally, the attention mechanism, LSTM, and GRU-based decoders have been used for predicting hate speech categories. Among the three encoder–decoder algorithms, attention-based decoder obtained the best accuracy (77%). |
| Databáze: | OpenAIRE |
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