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dc.contributor.authorChen, J
dc.contributor.authorBecken, S
dc.contributor.authorStantic, B
dc.date.accessioned2020-04-27T01:43:02Z
dc.date.available2020-04-27T01:43:02Z
dc.date.issued2018
dc.identifier.isbn9781450354899
dc.identifier.doi10.1145/3227609.3227680
dc.identifier.urihttp://hdl.handle.net/10072/383961
dc.description.abstractThe growing number of social media users and their posts provide valuable data about the sentiment that they have toward different services as well as people. Recent advances in big data analytics and natural language processing provided means to automatically calculate sentiment in text. Sentiment analysis is method which can be used to analyze social media content, it basically converts social media post text into quantitative data. While significant work was directed toward sentiment analytics of English text there is limited attention toward sentiment analytic of Chinese language. In this work we propose and test method to identify sentiment in Chinese social media posts and to test our method we rely on posts sent by visitors of Great Barrier Reef by users of most popular Chinese social media platform Sina Weibo. We elaborate process of capturing, managing and also we describe method for sentiment calculation, which provided details of sentiment toward the different GBR destinations and demonstrate that the proposed method is effective to obtain information and to monitor visitors’ opinion.
dc.description.peerreviewedYes
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.ispartofconferencename8th International Conference on Web Intelligence, Mining and Semantics
dc.relation.ispartofconferencetitleWIMS '18: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics
dc.relation.ispartofdatefrom2018-06-25
dc.relation.ispartofdateto2018-06-27
dc.relation.ispartoflocationNovi Sad, Serbia
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode4602
dc.titleSentiment analytics of Chinese social media posts
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, ISBN: 978-1-4503-5489-9, https://doi.org/10.1145/3227609.3227680
gro.hasfulltextFull Text
gro.griffith.authorBecken, Susanne
gro.griffith.authorStantic, Bela


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