Large-scale Arabic text classification using MapReduce

Other Title(s)

تصنيف النص العربي واسع النطاق باستخدام نموذج MapReduce

Dissertant

Abu Shab, Mahir Mahmud Ali

Thesis advisor

Barakah, Ribhi Sulayman

Comitee Members

al-Halis, Ala Mustafa
Makki, Muhammad Amin

University

Islamic University

Faculty

Faculty of Information Technology

University Country

Palestine (Gaza Strip)

Degree

Master

Degree Date

2015

English Abstract

Text classification on large-scale real documents has become one of the most core problems in text mining.

For English and other languages many text classification works have been done with high performance.

However, Arabic language still needs more attention and research since it is highly rich and requires special processing.

Existing Arabic text classification approaches use techniques such as feature selection, data representation, feature extraction and sequential algorithms.

Few attempts were done to classify large-scale Arabic text document in a parallel manner.

In our research, we propose a parallel classification approach based on the Naïve Bayes algorithm for large volume Arabic text using MapReduce with enhanced speedup and preserved accuracy.

The experiments show that the parallel classification approach can process large volume of Arabic text efficiently on a MapReduce cluster and significantly improves speedup up to 12 times better than the sequential approach using the same classification algorithm.

Also, classification results show that the proposed parallel classifier has preserved accuracy up to 97%.

Main Subjects

Languages & Comparative Literature
Information Technology and Computer Science
Arabic language and Literature

Topics

• Algorithms
• Data processing
• Arabic language
• Data mining
• Big data

No. of Pages

65

Table of Contents

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-688536