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DCU: aspect-based polarity classification for SemEval task 4

Wagner, Joachim orcid logoORCID: 0000-0002-8290-3849, Arora, Piyush orcid logoORCID: 0000-0002-4261-2860, Cortes, Santiago, Barman, Utsab, Bogdanova, Dasha, Foster, Jennifer orcid logoORCID: 0000-0002-7789-4853 and Tounsi, Lamia (2014) DCU: aspect-based polarity classification for SemEval task 4. In: International Workshop on Semantic Evaluation (SemEval-2014), 23-24 Aug 2014, Dublin, Ireland. ISBN 978-1-941643-24-2

Abstract
We describe the work carried out by DCU on the Aspect Based Sentiment Analysis task at SemEval 2014. Our team submitted one constrained run for the restaurant domain and one for the laptop domain for sub-task B (aspect term polarity prediction), ranking highest out of 36 systems on the restaurant test set and joint highest out of 32 systems on the laptop test set.
Metadata
Item Type:Conference or Workshop Item (Poster)
Event Type:Workshop
Refereed:Yes
Uncontrolled Keywords:Sentiment analysis; Opinion mining; Attitude: Polarity
Subjects:Computer Science > Computational linguistics
Business > Consumer behaviour
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Next Generation Localisation (CNGL)
Research Initiatives and Centres > National Centre for Language Technology (NCLT)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). . Association for Computational Linguistics and Dublin City University. ISBN 978-1-941643-24-2
Publisher:Association for Computational Linguistics and Dublin City University
Official URL:http://www.aclweb.org/anthology/S14-2036
Copyright Information:This work is licensed under a Creative Commons Attribution 3.0 International Licence.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland
ID Code:20324
Deposited On:21 Jul 2015 10:23 by Joachim Wagner . Last Modified 22 Oct 2019 14:27
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