Sexism detection: The first corpus in Algerian dialect with a code-switching in Arabic/ French and English

• URL: http://arxiv.org/abs/2104.01443v1
• Date: Sat, 3 Apr 2021 16:34:51 GMT
• Title: Sexism detection: The first corpus in Algerian dialect with a code-switching in Arabic/ French and English
• Authors: Imane Guellil and Ahsan Adeel and Faical Azouaou and Mohamed Boubred and Yousra Houichi and Akram Abdelhaq Moumna
• Abstract summary: A new hate speech corpus (Arabic_fr_en) is developed using three different annotators. For corpus validation, three different machine learning algorithms are used, including deep Convolutional Neural Network (CNN), long short-term memory (LSTM) network and Bi-directional LSTM (Bi-LSTM) network. Simulation results demonstrate the best performance of the CNN model, which achieved F1-score up to 86% for the unbalanced corpus.
• Score: 0.3425341633647625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, an approach for hate speech detection against women in Arabic community on social media (e.g. Youtube) is proposed. In the literature, similar works have been presented for other languages such as English. However, to the best of our knowledge, not much work has been conducted in the Arabic language. A new hate speech corpus (Arabic\_fr\_en) is developed using three different annotators. For corpus validation, three different machine learning algorithms are used, including deep Convolutional Neural Network (CNN), long short-term memory (LSTM) network and Bi-directional LSTM (Bi-LSTM) network. Simulation results demonstrate the best performance of the CNN model, which achieved F1-score up to 86\% for the unbalanced corpus as compared to LSTM and Bi-LSTM.

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