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dc.contributor.authorMedagoda, N
dc.contributor.authorShanmuganathan, S
dc.contributor.authorWhalley, JL
dc.date.accessioned2013-12-09T18:44:51Z
dc.date.accessioned2013-12-09T18:45:01Z
dc.date.accessioned2013-12-09T18:50:48Z
dc.date.accessioned2013-12-09T18:50:55Z
dc.date.accessioned2013-12-09T18:51:10Z
dc.date.accessioned2013-12-09T18:51:23Z
dc.date.accessioned2013-12-09T18:51:56Z
dc.date.accessioned2013-12-09T18:52:53Z
dc.date.accessioned2013-12-09T18:53:03Z
dc.date.available2013-12-09T18:44:51Z
dc.date.available2013-12-09T18:45:01Z
dc.date.available2013-12-09T18:50:48Z
dc.date.available2013-12-09T18:50:55Z
dc.date.available2013-12-09T18:51:10Z
dc.date.available2013-12-09T18:51:23Z
dc.date.available2013-12-09T18:51:56Z
dc.date.available2013-12-09T18:52:53Z
dc.date.available2013-12-09T18:53:03Z
dc.date.copyright2013-12-11
dc.date.issued2013-12-10
dc.identifier.citationIn Advances in ICT for Emerging Regions (ICTer), 2013 International Conference on (pp. 144-148). IEEE.
dc.identifier.urihttp://hdl.handle.net/10292/6184
dc.description.abstractIn the past decade many opinion mining and sentiment classification studies have been carried out for opinions in English. However, the amount of work done for non-English text opinions is very limited.In this review, we investigate opinion mining and sentiment classification studies in three non-English languages to find the classification methods and the efficiency of each algorithm used in these methods. It is found that most of the research conducted for non-English has followed the methods used in the English language with onlylimited usage of language specific properties, such as morphological variations. The application domains seem to be restricted to particular fields and significantly less research has been conducted in cross domains. Keywords—Natural Language processing, Text mining, Machine Learning.
dc.publisherIEEE
dc.relation.replaceshttp://hdl.handle.net/10292/6176
dc.relation.replaces10292/6176
dc.relation.replaceshttp://hdl.handle.net/10292/6177
dc.relation.replaces10292/6177
dc.relation.replaceshttp://hdl.handle.net/10292/6178
dc.relation.replaces10292/6178
dc.relation.replaceshttp://hdl.handle.net/10292/6179
dc.relation.replaces10292/6179
dc.relation.replaceshttp://hdl.handle.net/10292/6180
dc.relation.replaces10292/6180
dc.relation.replaceshttp://hdl.handle.net/10292/6181
dc.relation.replaces10292/6181
dc.relation.replaceshttp://hdl.handle.net/10292/6182
dc.relation.replaces10292/6182
dc.relation.replaceshttp://hdl.handle.net/10292/6183
dc.relation.replaces10292/6183
dc.relation.isreplacedby10292/7874
dc.relation.isreplacedbyhttp://hdl.handle.net/10292/7874
dc.relation.urihttp://ieeexplore.ieee.org/document/6761169/
dc.rightsCopyright © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectNatural Language processing
dc.subjectText mining
dc.subjectMachine Learning
dc.titleA Comparative Analysis of Opinion Mining and Sentiment Classification in Non-english Languages
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1109/ICTer.2013.6761169
aut.conference.typePaper Published in Proceedings
pubs.elements-id158994


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