An Implementation of Support Vector Machine on the Multi-Label Classification of English-Translated Quranic Verses

Abstract

One of the attempts to understand the meaning and content of the Quran, the central religious text of Islam, is the topic classification of Quranic verses. Verse topic classification aims to help the reader, so he can easily and quickly find information or knowledge contained in the Quran. In this paper, we build a classification model for the topics of English- translated Quranic verses using Support Vector Machine (SVM). The problem of classification of topics of Quranic verses is categorized as a multi-label classification problem. Hence, we design an SVM-based classifier to solve the multi-label classification of topics of Quranic verses. We also implement several techniques such as preprocessing, feature extraction, and dimensionality reduction to solve this problem. Then, we use Hamming Loss as a performance measure to evaluate our proposed classifier model. We find that our proposed model yields outstanding results.