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2021 | OriginalPaper | Chapter

Hadith Arabic Text Classification Using Convolutional Neural Network and Support Vector Machine

Authors : Irwan Mazlin, Izani Mohamed Rawi, Zaki Zakaria

Published in: Computational Science and Technology

Publisher: Springer Singapore

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Abstract

There are a lot of work has been implemented to solve the problem of text classification but There is only few researchers doing Arabic text classification because of the difficulties in text preprocessing. Convolution Neural network and support vector machine is two different algorithm that can be applied on text classification. CNN seems to be good in extracting the feature from input and SVM is good for classify the class. This study is to introduce Hadith text classification using Convolutional Neural Network and Support Vector Machine. In order to get preliminary result, we used BBC news article (English language) and Arabic tweet sentiment (Arabic language) as dataset for CNN with SVM model. There are 4 methods to evaluate the model which are f1-score, precision and recall and accuracy and error rate probability. We evaluate the model using accuracy and loss using different learning rate. The model accuracy and loss for preliminary result of BBC news article (English language) and Arabic tweet sentiment(Arabic language) are 0.857 accuracy, 0.245 loss and 0.884 accuracy, 0.344 loss. This shows that the proposed model has potential for Hadith text classification.

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Metadata
Title
Hadith Arabic Text Classification Using Convolutional Neural Network and Support Vector Machine
Authors
Irwan Mazlin
Izani Mohamed Rawi
Zaki Zakaria
Copyright Year
2021
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-33-4069-5_30

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