adv. artif. intell. res. Advances in Artificial Intelligence Research 2757-7422 Osman ÖZKARACA 10.54569/aair.1549781 Machine Learning (Other) Natural Language Processing Makine Öğrenme (Diğer) Doğal Dil İşleme A Comparative Study of Machine Learning Classifiers for Different Language Spam SMS Detection: Performance Evaluation and Analysis A Comparative Study of Machine Learning Classifiers for Different Language Spam SMS Detection: Performance Evaluation and Analysis https://orcid.org/0009-0009-7647-0731 Dev Sharma Samrat Kumar Jagannath University 12 30 2024 4 2 69 77 09 13 2024 12 28 2024 Copyright © 2020, Advances in Artificial Intelligence Research 2020 Advances in Artificial Intelligence Research

With the continuous rise in the number of mobile device users, SMS (Short Message Service) remains a prevalent communication tool accessible on both smartphones and basic phones. Consequently, SMS traffic has experienced a significant surge. This increase has also led to a rise in spam messages, as spammers seek financial or business gains through activities like marketing promotions, lottery scams, and credit card information theft. Consequently, spam classification has become a focal point of research. In this paper, we explore the effectiveness of 11 machine learning algorithms for SMS spam detection, including multinomial Naïve Bayes, K-Nearest Neighbors (KNN), and Random Forest, among others. Utilizing datasets from UCI and Bangla SMS collections, our experimental results reveal that the multinomial Naïve Bayes algorithm surpasses previous models in spam detection, achieving accuracies of 98.65% and 89.10% in the respective datasets.

With the continuous rise in the number of mobile device users, SMS (Short Message Service) remains a prevalent communication tool accessible on both smartphones and basic phones. Consequently, SMS traffic has experienced a significant surge. This increase has also led to a rise in spam messages, as spammers seek financial or business gains through activities like marketing promotions, lottery scams, and credit card information theft. Consequently, spam classification has become a focal point of research. In this paper, we explore the effectiveness of 11 machine learning algorithms for SMS spam detection, including multinomial Naïve Bayes, K-Nearest Neighbors (KNN), and Random Forest, among others. Utilizing datasets from UCI and Bangla SMS collections, our experimental results reveal that the multinomial Naïve Bayes algorithm surpasses previous models in spam detection, achieving accuracies of 98.65% and 89.10% in the respective datasets.

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