Automatic Hate Speech Detection Using CNN Model and Word Embedding

Authors

  • Olumide Ebenezer Ojo Instituto Politécnico Nacional
  • Thang Ta Hoang Instituto Politécnico Nacional
  • Alexander Gelbukh Instituto Politécnico Nacional
  • Hiram Calvo Instituto Politécnico Nacional
  • Grigori Sidorov Instituto Politécnico Nacional
  • Olaronke Oluwayemisi Adebanji Dalat University

DOI:

https://doi.org/10.13053/cys-26-2-4107

Keywords:

Hate speech, GloVe, 1D-CNN

Abstract

Hatred spreading through the use of language on social media platforms and in online groups is becoming a well-known phenomenon. By comparing two text representations: bag of words (BoW) and pre-trained word embedding using GloVe, we used a binary classification approach to automatically process user contents to detect hate speech. The Naive Bayes Algorithm (NBA), Logistic Regression Model (LRM), Support Vector Machines (SVM), Random Forest Classifier (RFC) and the one-dimensional Convolutional Neural Networks (1D-CNN) are the models proposed. With a weighted macro-F1 score of 0.66 and a 0.90 accuracy, the performance of the 1D-CNN and GloVe embeddings was best among all the models.

Author Biographies

Alexander Gelbukh, Instituto Politécnico Nacional

Grigori Sidorov, Instituto Politécnico Nacional

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Published

2022-06-15

Issue

Vol. 26 No. 2 (2022): Emerging Issues and Applications of Fuzzy Systems, Neural Networks and Metaheuristics (Castillo, Melin, Montiel, Valdez)

Section

Articles

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