Arabic Hate Speech Detection Using Different Machine Learning Approach

Muaad, Abdullah Y.; Hanumanthappa, J.; Prakash, S. P. Shiva; Al-Sarem, Mohammed; Ghabban, Fahad; Benifa, J. V. Bibal; Chola, Channabasava

doi:10.1007/978-3-031-36258-3_38

Cited by 2 publications

(1 citation statement)

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“…Specifically, we will evaluate the effectiveness of the Bag-of-Words (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF) representations. Further, the performance of different classification techniques, such as the Logistic Regression Classifier (LRC), Random Forest Classifier (RFC), and Extra Trees Classifier (ETC) [17,18], to choose optimal features using a GA is discussed. The ensemble feature selection method helps us to find the optimal feature subset from several feature subsets through an integration method [19].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Arabic Multilabel Text Classification Approach Using Genetic Algorithm and Ensemble Learning

Alzanin,

Gumaei,

Haque

et al. 2023

Applied Sciences

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Multilabel classification of Arabic text is an important task for understanding and analyzing social media content. It can enable the categorization and monitoring of social media posts, the detection of important events, the identification of trending topics, and the gaining of insights into public opinion and sentiment. However, multilabel classification of Arabic contents can present a certain challenge due to the high dimensionality of the representation and the unique characteristics of the Arabic language. In this paper, an effective approach is proposed for Arabic multilabel classification using a metaheuristic Genetic Algorithm (GA) and ensemble learning. The approach explores the effect of Arabic text representation on classification performance using both Bag of Words (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF) methods. Moreover, it compares the performance of ensemble learning methods such as the Extra Trees Classifier (ETC) and Random Forest Classifier (RFC) against a Logistic Regression Classifier (LRC) as a single and ensemble classifier. We evaluate the approach on a new public dataset, namely, the MAWQIF dataset. The MAWQIF is the first multilabel Arabic dataset for target-specific stance detection. The experimental results demonstrate that the proposed approach outperforms the related work on the same dataset, achieving 80.88% for sentiment classification and 68.76% for multilabel tasks in terms of the F1-score metric. In addition, the data augmentation with feature selection improves the F1-score result of the ETC from 65.62% to 68.80%. The study shows the ability of the GA-based feature selection with ensemble learning to improve the classification of multilabel Arabic text.

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Section: Introductionmentioning

confidence: 99%