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

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.