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Reseach Article

Emotion Classification in Arabic Poetry using Machine Learning

by Ouais Alsharif, Deema Alshamaa, Nada Ghneim
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Number 16
Year of Publication: 2013
Authors: Ouais Alsharif, Deema Alshamaa, Nada Ghneim

Ouais Alsharif, Deema Alshamaa, Nada Ghneim . Emotion Classification in Arabic Poetry using Machine Learning. International Journal of Computer Applications. 65, 16 ( March 2013), 10-15. DOI=10.5120/11006-6300

@article{ 10.5120/11006-6300,
author = { Ouais Alsharif, Deema Alshamaa, Nada Ghneim },
title = { Emotion Classification in Arabic Poetry using Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 65 },
number = { 16 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 10-15 },
numpages = {9},
url = { },
doi = { 10.5120/11006-6300 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T21:20:34.353088+05:30
%A Ouais Alsharif
%A Deema Alshamaa
%A Nada Ghneim
%T Emotion Classification in Arabic Poetry using Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 65
%N 16
%P 10-15
%D 2013
%I Foundation of Computer Science (FCS), NY, USA

In recent years, work on sentiment analysis and automatic text classification in Arabic has seen some progress. However, the problem of emotion classification remains widely under-researched. This work attempts to remedy the situation by considering the problem of classifying documents by their overall sentiment into four affect categories that are present in Arabic poetry- Retha, Ghazal, Fakhr and Heja. This work begins by building an emotional annotated Arabic poetry corpus. The impact of different levels of language preprocessing settings, feature vector dimensions and machine learning algorithms is, then, investigated and evaluated on the emotion classi?cation task.

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Index Terms

Computer Science
Information Sciences


Natural language processing classification machine learning sentiment analysis