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Classifying sentiment in microblogs: is brevity an advantage?

Bermingham, Adam and Smeaton, Alan F. (2010) Classifying sentiment in microblogs: is brevity an advantage? In: CIKM 2010 - 19th International Conference on Information and Knowledge Management, 26-30 October 2010, Toronto, Canada. ISBN 978-1-4503-0099-5

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Microblogs as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. However, this short length coupled with their noisy nature can pose difficulties for standard machine learning document representations. In this work we examine the hypothesis that it is easier to classify the sentiment in these short form documents than in longer form documents. Surprisingly, we find classifying sentiment in microblogs easier than in blogs and make a number of observations pertaining to the challenge of supervised learning for sentiment analysis in microblogs.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:sentiment analysis;
Subjects:Computer Science > Computational linguistics
Computer Science > Machine learning
Computer Science > Artificial intelligence
Computer Science > World Wide Web
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Published in:Proceedings of the 19th ACM international conference on Information and knowledge management. . Association for Computing Machinery. ISBN 978-1-4503-0099-5
Publisher:Association for Computing Machinery
Official URL:
Copyright Information:Copyright © 2010 Association for Computing Machinery
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:15663
Deposited On:25 Nov 2010 15:49 by Adam Bermingham. Last Modified 13 Jan 2017 11:21

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