Irony in online reviews: A linguistic approach to identifying irony

dc.contributor.author	Jönsson, Maria
dc.date.accessioned	2010-08-04T11:19:45Z
dc.date.available	2010-08-04T11:19:45Z
dc.date.issued	2010-08-04
dc.identifier.uri	http://hdl.handle.net/2077/23031
dc.description.abstract	Several NLP-applications could benefit from identifying irony. Currently there is no process for doing so automatically. My findings suggest that irony occurs in up to 8.5% of online reviews. I identify three groups of irony based on the linguistic features they exhibit. I predict the irony in two of these groups are possible to identify automatically, covering 70% of the irony in my corpus. If my findings can be verified in a more exstensive investigation, I suspect my ideas can be applied to other domains than online reviews as well.	sv
dc.language.iso	eng	sv
dc.relation.ispartofseries	SPL kandidatuppsats i engelska	sv
dc.relation.ispartofseries	SPL2010-006	sv
dc.subject	sentiment classification	sv
dc.subject	irony	sv
dc.subject	sarcasm	sv
dc.subject	opinion mining	sv
dc.subject	semantic polarity	sv
dc.subject	review	sv
dc.title	Irony in online reviews: A linguistic approach to identifying irony	sv
dc.type	Text
dc.setspec.uppsok	HumanitiesTheology
dc.type.uppsok	M2
dc.contributor.department	University of Gothenburg/Department of Languages and Literatures	eng
dc.contributor.department	Göteborgs universitet/Institutionen för språk och litteraturer	swe
dc.type.degree	Student essay