Automatic irony- and sarcasm detection in Social media
Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
This thesis looks at different methods that have been used for irony and sarcasm detection and also includes the design and programming of a machine learning model that classifies text as sarcastic or non-sarcastic. This is done with supervised learning. Two different data set where used, one with Amazon reviews and one from Twitter. An accuracy of 87% was obtained on the Amazon data with the Support Vector Machine. For the Twitter data was an accuracy of 71% obtained with the Adaboost classifier was used. The thesis is done in collaboration with Gavagai AB, which is company working with Big-data text with expertise in semantic analysis and opinion mining.
Place, publisher, year, edition, pages
2015. , 45 p.
UPTEC F, ISSN 1401-5757 ; 15045
Other Engineering and Technologies not elsewhere specified
IdentifiersURN: urn:nbn:se:uu:diva-262242OAI: oai:DiVA.org:uu-262242DiVA: diva2:852975
Master Programme in Engineering Physics
Nyberg, TomasAshcroft, Michael