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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/7478
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dc.contributor.authorSirbu, Dorinela-
dc.contributor.authorSecui, Ana-
dc.contributor.authorDascalu, Mihai-
dc.contributor.authorCrossley, Scott-
dc.contributor.authorRuseti, Stefan-
dc.contributor.authorTrausan-Matu, Stefan-
dc.date.accessioned2017-02-10T12:42:02Z-
dc.date.available2017-02-10T12:42:02Z-
dc.date.issued2017-02-
dc.identifier.citationSirbu, M. D., Secui, A., Dascalu, M., Crossley, S. A., Ruseti, S., & Trausan-Matu, S. (2016). Extracting Gamers' Opinions from Reviews. In 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2016) (pp. 227–232). Timisoara, Romania: IEEEen_US
dc.identifier.urihttp://hdl.handle.net/1820/7478-
dc.description.abstractOpinion mining and sentiment analysis are a trending research domain in Natural Language Processing focused on automatically extracting subjective information, feelings, opinions, ideas or emotions from texts. Our study is centered on identifying sentiments and opinions, as well as other latent linguistic dimensions expressed in on-line game reviews. Over 9500 entertainment game reviews from Amazon were examined using a Principal Component Analysis applied to word-count indices derived from linguistic resources. Eight affective components were identified as being the most representative semantic and sentiment-oriented dimensions for our dataset. These components explained 51.2% of the variance of all reviews. A Multivariate Analysis of Variance showed that five of the eight components demonstrated significant differences between positive, negative and neutral game reviews. These five components used as predictors in a Discriminant Function Analysis, were able to classify game reviews into positive, negative and neutral ratings with a 55% accuracy.en_US
dc.description.sponsorshipThis study is part of the RAGE project. The RAGE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.en_US
dc.publisherIEEEen_US
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/644187/EU/Realising an Applied Gaming Eco-system/RAGEen_US
dc.rightsopenAccessen_US
dc.subjectNatural Language Processingen_US
dc.subjectsentiment analysisen_US
dc.subjectopinion miningen_US
dc.subjectgame reviewsen_US
dc.subjectlexical analysisen_US
dc.titleExtracting Gamers' Opinions from Reviewsen_US
dc.typeconferenceObjecten_US
Appears in Collections:1. RAGE Publications

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