Medagoda, NShanmuganathan, SWhalley, JL2013-12-092013-12-092013-12-092013-12-092013-12-092013-12-092013-12-092013-12-092013-12-092013-12-092013-12-092013-12-092013-12-092013-12-092013-12-092013-12-092013-12-092013-12-092013-12-112013-12-11In Advances in ICT for Emerging Regions (ICTer), 2013 International Conference on (pp. 144-148). IEEE.https://hdl.handle.net/10292/6184In 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.Copyright © 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.Natural Language processingText miningMachine LearningConference ContributionOpenAccess10.1109/ICTer.2013.6761169