Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/108776
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dc.contributor.authorAhmed, K.-
dc.contributor.authorTazi, N.-
dc.contributor.authorHossny, A.-
dc.date.issued2015-
dc.identifier.citationConference proceedings / IEEE International Conference on Systems, Man, and Cybernetics. IEEE International Conference on Systems, Man, and Cybernetics, 2015, pp.2174-2179-
dc.identifier.isbn9781479986965-
dc.identifier.issn1062-922X-
dc.identifier.urihttp://hdl.handle.net/2440/108776-
dc.description.abstractThe rapid increase in data on social media creates a need for mining such data to get valuable insights. The data type can be unstructured with large volumes. Sentiment analysis addresses such need by detecting opinions or emotions on the social media text. Sentiment analysis can be performed in various domains such as social, medical and industrial applications. This paper presents a survey about sentiment analysis addressing the different concepts in this area, problems and its solutions, available APIs, tools used and presenting a list of open challenges in this area.-
dc.description.statementofresponsibilityKhaled Ahmed, Neamat El Tazi, Ahmad Hany Hossny-
dc.language.isoen-
dc.publisherIEEE-
dc.relation.ispartofseriesIEEE International Conference on Systems Man and Cybernetics Conference Proceedings-
dc.rights© 2015 IEEE-
dc.source.urihttp://dx.doi.org/10.1109/smc.2015.380-
dc.subjectSentiment lexicons and emotion detection; social media; sentiment analysis; feature selection; recommendation; spam detection-
dc.titleSentiment analysis over social networks: an overview-
dc.typeConference paper-
dc.contributor.conference2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2015) (9 Oct 2015 - 12 Oct 2015 : Hong Kong)-
dc.identifier.doi10.1109/SMC.2015.380-
pubs.publication-statusPublished-
dc.identifier.orcidHossny, A. [0000-0002-5178-9211]-
Appears in Collections:Aurora harvest 3
Mathematical Sciences publications

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