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Detection of offensive terms in resource-poor language using machine learning algorithms.

Muhammad Owais Raza1, Naeem Ahmed Mahoto1, Mohammed Hamdi2

  • 1Department of Software Engineering, Mehran University of Engineering and Technology Jamshoro, Jamshoro, Pakistan.

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Summary
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This study developed a new method to automatically detect offensive terms in Urdu social media content. The approach improves accuracy for low-resource languages across platforms like Twitter and YouTube.

Keywords:
Classification modelMachine learningOffensive termsResource-poor language

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Area of Science:

  • Natural Language Processing (NLP)
  • Machine Learning
  • Computational Linguistics

Background:

  • Offensive user-generated content poses challenges for social media platforms.
  • Automated detection of offensive terms is difficult, especially for low-resource languages like Urdu.
  • Existing NLP efforts primarily focus on high-resource languages, leaving a gap for Urdu.

Purpose of the Study:

  • To introduce a combinatorial pre-processing approach for detecting offensive terms in Urdu.
  • To develop and evaluate a cross-platform classification model for Urdu offensive content.
  • To assess the performance of machine learning models with various pre-processing techniques for Urdu.

Main Methods:

  • Utilized datasets from Twitter and YouTube for training and testing.
  • Implemented decision tree, random forest, and naive Bayes algorithms.
  • Applied a combinatorial pre-processing approach combining techniques like stopword and punctuation removal.

Main Results:

  • The combinatorial pre-processing approach demonstrated effectiveness for Urdu offensive term detection.
  • Stopword removal achieved 83.27% accuracy when training on D1 and testing on D2.
  • Stopword and punctuation removal achieved 74.54% accuracy when training on D2 and testing on D1.
  • The proposed approach outperformed benchmarks, reaching 82.9% and 97.2% accuracy on datasets D1 and D2, respectively.

Conclusions:

  • The combinatorial pre-processing method is effective for building Urdu offensive term detection models.
  • Machine learning models show varying performance based on pre-processing combinations in a cross-platform setting.
  • This research contributes to addressing the challenge of offensive content detection in low-resource languages.