Barbosa Santillán, Liliana IbethRivera Hernández, José Antonio2025-04-252023Rivera Hernández, J. A. (2023). Un modelo SVM de clasificación de spam con Oracle Text para foros de publicaciones.[Tesis maestría].Instituto Tecnológico y de Estudios Superiores de Monterrey. Recuperado de: https://hdl.handle.net/11285/703544https://hdl.handle.net/11285/703544https://orcid.org/0000-0002-3509-9667The pervasive issue of spam, characterized by unsolicited and often irrelevant or inappropriate messages infiltrating the internet, presents substantial challenges within the realm of technical forums. This research is dedicated to a thorough exploration of the complexities surrounding spam and its impact on these forums. The disruptive nature of spam, with its tendency to clutter discussions and complicate information retrieval, has prompted the implementation of stringent measures in many technical forums. Acknowledging the dynamic and evolving nature of spamming behavior, along with the constant adaptation of content and methods employed by spammers, this study addresses the inherent challenges of the lack of adaptable spam databases suitable for automated classifiers. To bridge this gap, we meticulously crafted a spam database, labeled by content moderation experts, and categorized into spam and regular posts to ensure precise classification. Through this meticulous manual labeling process, a total of 1,916 posts were accurately identified as spam. Recognizing the need for a robust spam classification solution that seamlessly integrates into Oracle Database, SQL, and Oracle APEX applications without relying on external solutions, we embarked on the development of a sophisticated machine learning classifier. Our innovative Oracle Text SVM classifier emerged as a powerful solution, showcasing an impressive average accuracy of 90 percent during the validation phase. Further experimentation illuminated the potential of Oracle Text SVM classifiers for real-time applications, emphasizing their capacity to enhance classification efficiency by fine-tuning key Oracle Text features. This research not only contributes to a deeper understanding of spam within technical forums but also introduces a practical and efficient solution for seamlessly embedding spam classifiers within widely-used Oracle platforms.TextospaopenAccesshttp://creativecommons.org/licenses/by-nc/4.0INGENIERÍA Y TECNOLOGÍA::CIENCIAS TECNOLÓGICAS::TECNOLOGÍA DE LOS ORDENADORES::LENGUAJES ALGORÍTMICOSTechnologyTesis de Maestría / master ThesisEl trabajo tiene dentro de su contenido el procedimiento del modulo de moderación de contenido el cual es una parte muy importante de la seguridad del Foro de Oracle y a petición de la empresa Oracle, deberé de evitar que sea publicado durante un tiempo pertinente, así que pido que este documente quedo bajo embargo hasta que el modulo haya sido deprecado en un futuro.https://orcid.org/0009-0008-8474-0036SVMmachine learningArtificial intelligenceClassifierAprendizaje automáticoSupport vector machinesSPAMInteligencia artificialModelo de clasificaciónClasificadorClasificación de SPAM