Sentiment analysis in cross-linguistic context: How can machine translation influence sentiment classification?
In recent years, there has been a lot of interest in cross-language sentiment classification, as the research in sentiment analysis has shifted focus from English to less resourceful languages. Cross-language sentiment classification attempts to leverage the automated machine translation (MT) capabi...
| Published in: | Digital Scholarship in the Humanities Vol. 38; no. 1; pp. 23 - 34 |
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| Main Authors: | , |
| Format: | Article |
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Oxford University Press / USA
Apr2023
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=162941110&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 162941110 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Apr2023 vid: 38 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 162941110 10.1093/llc/fqac053 ppf: 23 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P size: 547KB tig: atl: Sentiment analysis in cross-linguistic context: How can machine translation influence sentiment classification? aug: au: Bilianos, Dimitris Mikros, George affil: Department of Italian Language and Literature, School of Philosophy, National and Kapodistrian University of Athens , Athens, Greece College of Humanities and Social Sciences, Hamad Bin Khalifa University , Doha, Qatar su: Machine translating Sentiment analysis Naive Bayes classification Electric machines English language sug: subj: Machine translating Sentiment analysis Naive Bayes classification Electric machines English language ab: In recent years, there has been a lot of interest in cross-language sentiment classification, as the research in sentiment analysis has shifted focus from English to less resourceful languages. Cross-language sentiment classification attempts to leverage the automated machine translation (MT) capability utilizing the infrastructure of languages rich in linguistic resources, mainly English, to help build sentiment analysis systems for low-resource languages. In this study, we explore how MT influences cross-language sentiment classification. To this end, we perform three different experiments, obtaining promising results. In the first experiment, we automatically translate 4,000 positive and negative reviews from English into Greek and Italian, thus obtaining labeled sentiment datasets in these languages. Then, we train a Naive Bayes classifier and compare the performance with the source dataset. In the second experiment, the translated reviews are automatically translated back into the source language (English), aiming to compare the classification accuracy with the one obtained in the original dataset. In the final approach, the reviews are translated from the source (English) into Italian through an intermediate translation in Greek to examine whether the performance was further diminished compared with the approach of the first experiment. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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