Emotion Classification in Arabic Poetry using Machine Learning

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 over...

Full description

Saved in:
Bibliographic Details
Published in:International journal of computer applications 2013-01, Vol.65 (16)
Main Authors: Alsharif, Ouais, Alshamaa, Deema, Ghneim, Nada
Format: Article
Language:English
Subjects:
Classification
Emotions
Machine learning
Mathematical analysis
Preprocessing
Remedies
Tasks
Texts
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue 16
container_start_page
container_title International journal of computer applications
container_volume 65
creator Alsharif, Ouais
Alshamaa, Deema
Ghneim, Nada
description 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.
doi_str_mv 10.5120/11006-6300
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_miscellaneous_1349431392</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2920955491</sourcerecordid><originalsourceid>FETCH-LOGICAL-p612-d7c7768ac6b9943ac7619c67cab13a33c38a30e5d816b794c8a95e4054f82bda3</originalsourceid><addsrcrecordid>eNpdj81LxDAQxYMouKx78S8IePFSTTr5vAhLWT-gooe9L9M01SzddG3ag_-9QT2I7zLzhh-PeYRccnYjecluOWdMFQoYOyELZrUsjDH69M9-TlYp7VkW2FJZsSB3m8MwhSHSqseUQhccftsQ6XrEJjj6Ovhp_KRzCvGNPqN7D9HT2uMY8-GCnHXYJ7_6nUuyvd9sq8eifnl4qtZ1cVS8LFrttFYGnWqsFYBOK26d0g4bDgjgwCAwL1vDVaOtcAat9IJJ0ZmyaRGW5Pon9jgOH7NP0-4QkvN9j9EPc9pxEDmX51IZvfqH7od5jPm5THGtJIBl8AWWXVbV</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1317653390</pqid></control><display><type>article</type><title>Emotion Classification in Arabic Poetry using Machine Learning</title><source>Freely Accessible Science Journals - check A-Z of ejournals</source><creator>Alsharif, Ouais ; Alshamaa, Deema ; Ghneim, Nada</creator><creatorcontrib>Alsharif, Ouais ; Alshamaa, Deema ; Ghneim, Nada</creatorcontrib><description>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.</description><identifier>ISSN: 0975-8887</identifier><identifier>EISSN: 0975-8887</identifier><identifier>DOI: 10.5120/11006-6300</identifier><language>eng</language><publisher>New York: Foundation of Computer Science</publisher><subject>Classification ; Emotions ; Machine learning ; Mathematical analysis ; Preprocessing ; Remedies ; Tasks ; Texts</subject><ispartof>International journal of computer applications, 2013-01, Vol.65 (16)</ispartof><rights>Copyright Foundation of Computer Science 2013</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Alsharif, Ouais</creatorcontrib><creatorcontrib>Alshamaa, Deema</creatorcontrib><creatorcontrib>Ghneim, Nada</creatorcontrib><title>Emotion Classification in Arabic Poetry using Machine Learning</title><title>International journal of computer applications</title><description>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.</description><subject>Classification</subject><subject>Emotions</subject><subject>Machine learning</subject><subject>Mathematical analysis</subject><subject>Preprocessing</subject><subject>Remedies</subject><subject>Tasks</subject><subject>Texts</subject><issn>0975-8887</issn><issn>0975-8887</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNpdj81LxDAQxYMouKx78S8IePFSTTr5vAhLWT-gooe9L9M01SzddG3ag_-9QT2I7zLzhh-PeYRccnYjecluOWdMFQoYOyELZrUsjDH69M9-TlYp7VkW2FJZsSB3m8MwhSHSqseUQhccftsQ6XrEJjj6Ovhp_KRzCvGNPqN7D9HT2uMY8-GCnHXYJ7_6nUuyvd9sq8eifnl4qtZ1cVS8LFrttFYGnWqsFYBOK26d0g4bDgjgwCAwL1vDVaOtcAat9IJJ0ZmyaRGW5Pon9jgOH7NP0-4QkvN9j9EPc9pxEDmX51IZvfqH7od5jPm5THGtJIBl8AWWXVbV</recordid><startdate>20130101</startdate><enddate>20130101</enddate><creator>Alsharif, Ouais</creator><creator>Alshamaa, Deema</creator><creator>Ghneim, Nada</creator><general>Foundation of Computer Science</general><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20130101</creationdate><title>Emotion Classification in Arabic Poetry using Machine Learning</title><author>Alsharif, Ouais ; Alshamaa, Deema ; Ghneim, Nada</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p612-d7c7768ac6b9943ac7619c67cab13a33c38a30e5d816b794c8a95e4054f82bda3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Classification</topic><topic>Emotions</topic><topic>Machine learning</topic><topic>Mathematical analysis</topic><topic>Preprocessing</topic><topic>Remedies</topic><topic>Tasks</topic><topic>Texts</topic><toplevel>online_resources</toplevel><creatorcontrib>Alsharif, Ouais</creatorcontrib><creatorcontrib>Alshamaa, Deema</creatorcontrib><creatorcontrib>Ghneim, Nada</creatorcontrib><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>International journal of computer applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alsharif, Ouais</au><au>Alshamaa, Deema</au><au>Ghneim, Nada</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Emotion Classification in Arabic Poetry using Machine Learning</atitle><jtitle>International journal of computer applications</jtitle><date>2013-01-01</date><risdate>2013</risdate><volume>65</volume><issue>16</issue><issn>0975-8887</issn><eissn>0975-8887</eissn><abstract>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.</abstract><cop>New York</cop><pub>Foundation of Computer Science</pub><doi>10.5120/11006-6300</doi></addata></record>
fulltext fulltext
identifier ISSN: 0975-8887
ispartof International journal of computer applications, 2013-01, Vol.65 (16)
issn 0975-8887
0975-8887
language eng
recordid cdi_proquest_miscellaneous_1349431392
source Freely Accessible Science Journals - check A-Z of ejournals
subjects Classification
Emotions
Machine learning
Mathematical analysis
Preprocessing
Remedies
Tasks
Texts
title Emotion Classification in Arabic Poetry using Machine Learning
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T20%3A53%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Emotion%20Classification%20in%20Arabic%20Poetry%20using%20Machine%20Learning&rft.jtitle=International%20journal%20of%20computer%20applications&rft.au=Alsharif,%20Ouais&rft.date=2013-01-01&rft.volume=65&rft.issue=16&rft.issn=0975-8887&rft.eissn=0975-8887&rft_id=info:doi/10.5120/11006-6300&rft_dat=%3Cproquest%3E2920955491%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-p612-d7c7768ac6b9943ac7619c67cab13a33c38a30e5d816b794c8a95e4054f82bda3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1317653390&rft_id=info:pmid/&rfr_iscdi=true