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dc.contributor.authorSafder, Iqra
dc.contributor.authorMehmood, Zainab
dc.contributor.authorSarwar, Raheem
dc.contributor.authorHassan, Saeed-Ul
dc.contributor.authorZaman, Farooq
dc.contributor.authorAdeel Nawab, Rao Muhammad
dc.contributor.authorBukhari, Faisal
dc.contributor.authorAyaz Abbasi, Rabeeh
dc.contributor.authorAlelyani, Salem
dc.contributor.authorRadi Aljohani, Naif
dc.contributor.authorNawaz, Raheel
dc.date.accessioned2021-06-23T10:55:12Z
dc.date.available2021-06-23T10:55:12Z
dc.date.issued2021-06-28
dc.identifier.citationSafder, I., Mehmood, Z., Sarwar, R. et al. (2021) Sentiment analysis for Urdu online reviews using deep learning models. Expert Systems. https://doi.org/10.1111/exsy.12751en
dc.identifier.issn0266-4720en
dc.identifier.doi10.1111/exsy.12751
dc.identifier.urihttp://hdl.handle.net/2436/624143
dc.descriptionThis is an accepted manuscript of an article published by Wiley in Expert Systems, available online at https://doi.org/10.1111/exsy.12751 The accepted version of the publication may differ from the final published version.en
dc.description.abstractMost existing studies are focused on popular languages like English, Spanish, Chinese, Japanese, and others, however, limited attention has been paid to Urdu despite having more than 60 million native speakers. In this paper, we develop a deep learning model for the sentiments expressed in this under-resourced language. We develop an open-source corpus of 10,008 reviews from 566 online threads on the topics of sports, food, software, politics, and entertainment. The objectives of this work are bi-fold (1) the creation of a human-annotated corpus for the research of sentiment analysis in Urdu; and (2) measurement of up-to-date model performance using a corpus. For their assessment, we performed binary and ternary classification studies utilizing another model, namely LSTM, RCNN Rule-Based, N-gram, SVM, CNN, and LSTM. The RCNN model surpasses standard models with 84.98 % accuracy for binary classification and 68.56 % accuracy for ternary classification. To facilitate other researchers working in the same domain, we have open-sourced the corpus and code developed for this research.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherWileyen
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.12751en
dc.subjectdeep learning modelsen
dc.subjectsentiment analysisen
dc.subjectUrdu online reviewsen
dc.subjectartificial intelligenceen
dc.titleSentiment analysis for Urdu online reviews using deep learning modelsen
dc.typeJournal articleen
dc.identifier.journalExpert Systemsen
dc.date.updated2021-06-22T11:34:12Z
dc.date.accepted2021-05-30
rioxxterms.funderUniversity of wolverhamptonen
rioxxterms.identifier.projectUOW23062021RSen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2022-06-28en
refterms.dateFCD2021-06-23T10:54:35Z
refterms.versionFCDAM


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