Type

Conference Proceedings

Authors

Mark Hughes
Lamia Tounsi
Ron O'Brien
Jennifer van der Puil
Jennifer Foster
Akshat Bakliwal

Subjects

Linguistics

Topics
computational linguistics irish elections sentiment analysis classifier supervised learning sentiment classification political machine learning

Sentiment analysis of political tweets: towards an accurate classifier (2013)

Abstract We perform a series of 3-class sentiment classification experiments on a set of 2,624 tweets produced during the run-up to the Irish General Elections in February 2011. Even though tweets that have been labelled as sarcastic have been omitted from this set, it still represents a difficult test set and the highest accuracy we achieve is 61.6% using supervised learning and a feature set consisting of subjectivity-lexicon-based scores, Twitter- specific features and the top 1,000 most dis- criminative words. This is superior to various naive unsupervised approaches which use subjectivity lexicons to compute an overall sentiment score for a <tweet,political party> pair.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> Subject = Computer Science
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Computational linguistics
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing
Ireland -> Dublin City University -> Subject = Computer Science: Machine learning

Full list of authors on original publication

Mark Hughes, Lamia Tounsi, Ron O'Brien, Jennifer van der Puil, Jennifer Foster, Akshat Bakliwal

Experts in our system

1
Jennifer Foster
Dublin City University
Total Publications: 53