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Arabic Text Categorization Algorithm Using Vector Space Model

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Artificial Intelligence, Internet of Things, and Society 5.0

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1113))

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Abstract

Text categorization is one of the most important jobs in knowledge retrieval and data mining. This study seeks to investigate several vector space model (VSMs) variants using the K-Nearest Neighbor algorithm (k-NN) method. 242 abstract Arabic documents that they used was utilized in this paper. The comparison is often based on well-known text assessment metrics, recall calculation, precision measurement, and measurement F1. Cosine outperformed in tests conducted on Saudi data sets, according to the results.

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Correspondence to Mohamed Shajahan .

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Hanandeh, E., Shajahan, M. (2023). Arabic Text Categorization Algorithm Using Vector Space Model. In: Hannoon, A., Mahmood, A. (eds) Artificial Intelligence, Internet of Things, and Society 5.0. Studies in Computational Intelligence, vol 1113. Springer, Cham. https://doi.org/10.1007/978-3-031-43300-9_4

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