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Reliable General Purpose Sentiment Analysis of the Public Twitter Stream

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https://osnadocs.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2017092716282
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dc.contributor.advisorProf. Dr. Oliver Vornberger
dc.creatorHaldenwang, Nils
dc.date.accessioned2017-09-27T07:36:22Z
dc.date.available2017-09-27T07:36:22Z
dc.date.issued2017-09-27T07:36:22Z
dc.identifier.urihttps://osnadocs.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2017092716282-
dc.description.abstractGeneral purpose Twitter sentiment analysis is a novel field that is closely related to traditional Twitter sentiment analysis but slightly differs in some key aspects. The main difference lies in the fact that the novel approach considers the unfiltered public Twitter stream while most of the previous approaches often applied various filtering steps which are not feasible for many applications. Another goal is to yield more reliable results by only classifying a tweet as positive or negative if it distinctly consists of the respective sentiment and mark the remaining messages as uncertain. Traditional approaches are often not that strict. Within the course of this thesis it could be verified that the novel approach differs significantly from the traditional approach. Moreover, the experimental results indicated that the archetypical approaches could be transferred to the new domain but the related domain data is consistently sub par when compared to high quality in-domain data. Finally, the viability of the best classification algorithm could be qualitatively verified in a real-world setting that was also developed within the course of this thesis.eng
dc.rightsNamensnennung-NichtKommerziell-KeineBearbeitung 3.0 Unported-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/-
dc.subjectSentiment Analysiseng
dc.subjectText Analysiseng
dc.subjectData Miningeng
dc.subjectTwittereng
dc.subjectSocial Media Analysiseng
dc.subjectTwitter Sentiment Analysiseng
dc.subjectGeneral Purpose Twitter Sentiment Analysiseng
dc.subjectMachine Learningeng
dc.subject.ddc000 - Informatik, Wissen, Systeme
dc.titleReliable General Purpose Sentiment Analysis of the Public Twitter Streameng
dc.typeDissertation oder Habilitation [doctoralThesis]-
thesis.locationOsnabrück-
thesis.institutionUniversität-
thesis.typeDissertation [thesis.doctoral]-
thesis.date2017-09-19-
dc.contributor.refereeProf. Dr. Frank Köster
dc.subject.bk54.72 - Künstliche Intelligenz
dc.subject.bk54.82 - Textverarbeitung
dc.subject.ccsE.0 - GENERAL
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Appears in Collections:FB06 - E-Dissertationen

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