Comparing the performance of knowledge-based and machine-learning approaches for the detection of emotions in an english Text
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- dc.contributor.author Barnes, Jeremyca
- dc.date.accessioned 2015-10-09T10:27:23Z
- dc.date.available 2015-10-09T10:27:23Z
- dc.date.issued 2015-10-09
- dc.description Treball de fi de màster en Lingüística Teòrica i Aplicadaca
- dc.description Tutors: Juan María Garrido Almiñana i Antoni Badia i Cardús
- dc.description.abstract The detection of emotion and sentiment analysis are very hot topics at the moment and the detection of emotion from written text still remains a difficult subject of this area of research. The main approaches to this task are knowledge-based approaches and machine-learning approaches. This paper examines the performance of two approaches (a knowledge-based and a machine-learning approach) on a small corpus of chat text annotated with emotion labels. It will be shown that the machine-learning approach used in this experiment outperforms the knowledge-based approach in all aspects.ca
- dc.format.mimetype application/pdfca
- dc.identifier.uri http://hdl.handle.net/10230/24828
- dc.language.iso engca
- dc.rights Attribution-NonCommercial-NoDerivs 3.0 Spainca
- dc.rights.accessRights info:eu-repo/semantics/openAccessca
- dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/ca
- dc.subject.other Ensenyament assistit per ordinador
- dc.subject.other Llenguatge i emocions
- dc.subject.other Adquisició del coneixement (Sistemes experts)
- dc.subject.other Tractament del llenguatge natural (Informàtica)
- dc.title Comparing the performance of knowledge-based and machine-learning approaches for the detection of emotions in an english Textca
- dc.type info:eu-repo/semantics/masterThesisca