A hybrid TwinSVM-HHO model for multilingual spam review detection using sentiment features and pre-trained embeddings Al-Zoubi, Ala´ M. Mora García, Antonio Miguel Faris, Hossam Qaddoura, Raneem Multilingual analysis SPAM detection SPAM Review Sentiment Analysis Support Vector Machines SVM Harris Hawk Optimization HHO Embedding The detection of spam reviews in multilingual environments remains a challenging task due to linguistic diversity, data imbalance, and semantic complexity. This paper proposes a novel hybrid model that integrates Twin Support Vector Machine (TwinSVM) with Harris Hawks Optimization (HHO) for simultaneous parameter optimization and feature selection. To enhance semantic understanding, sentiment-based features are incorporated alongside pre-trained word embedding models—BERT, FastText, and MUSE—across English, Arabic, and Spanish datasets. Our approach generates 24 high-quality datasets using embeddings with 100 and 400 dimensions, including a combined multilingual set. Experimental results demonstrate that our proposed HHO-TwinSVM model consistently outperforms conventional classifiers and metaheuristic-enhanced SVMs, achieving accuracy improvements of up to 9.44 % and enhanced robustness in low-resource languages. This integrated framework represents a scalable and adaptable solution for multilingual spam detection. Four detailed experiments were conducted in this study, each designed to address and demonstrate a specific aspect of the proposed approach. Across all experiments, the method outperformed existing algorithms, achieving impressive accuracy rates of 92.9741 %, 89.0314 %, 80.3580 %, and 85.0859 % on Arabic, English, Spanish, and multilingual datasets, respectively. Subsequently, sentiment analysis features were incorporated to further enhance detection performance, resulting in improvements of 1.0994 %, 2.6674 %, 9.4430 %, and 8.7448 %, respectively. A comprehensive analysis of the experimental results, including the influence of reviews and sentiment features, is also presented. 2026-01-28T12:03:26Z 2026-01-28T12:03:26Z 2025-08-25 journal article Ala’ M. Al-Zoubi, Antonio M. Mora, Hossam Faris, Raneem Qaddoura, A hybrid TwinSVM-HHO model for multilingual spam review detection using sentiment features and pre-trained embeddings, Expert Systems with Applications, Volume 287, 2025, 128160, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2025.128160. (https://www.sciencedirect.com/science/article/pii/S0957417425017804) https://hdl.handle.net/10481/110424 https://doi.org/10.1016/j.eswa.2025.128160 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ embargoed access Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Elsevier