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Automatic hate speech detection using killer natural language processing optimizing ensemble deep learning approach

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Abstract

Over the last decade, the increased use of social media has led to an increase in hateful activities in social networks. Hate speech is one of the most dangerous of these activities, so users have to protect themselves from these activities from YouTube, Facebook, Twitter etc. This paper introduces a method for using a hybrid of natural language processing and with machine learning technique to predict hate speech from social media websites. After hate speech is collected, steaming, token splitting, character removal and inflection elimination is performed before performing hate speech recognition process. After that collected data is examined using a killer natural language processing optimization ensemble deep learning approach (KNLPEDNN). This method detects hate speech on social media websites using an effective learning process that classifies the text into neutral, offensive and hate language. The performance of the system is then evaluated using overall accuracy, f-score, precision and recall metrics. The system attained minimum deviations mean square error − 0.019, Cross Entropy Loss − 0.015 and Logarithmic loss L-0.0238 and 98.71% accuracy.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Research Group No. RG-1439-088.

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Authors and Affiliations

  1. Computer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia

    Zafer Al-Makhadmeh & Amr Tolba

  2. Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin-El-Kom, 32511, Egypt

    Amr Tolba

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  1. Zafer Al-Makhadmeh
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  2. Amr Tolba
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Correspondence to Zafer Al-Makhadmeh.

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Al-Makhadmeh, Z., Tolba, A. Automatic hate speech detection using killer natural language processing optimizing ensemble deep learning approach. Computing 102, 501–522 (2020). https://doi.org/10.1007/s00607-019-00745-0

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