Paper: Multilingual Subjectivity: Are More Languages Better?

ACL ID C10-1004
Title Multilingual Subjectivity: Are More Languages Better?
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010

While subjectivity related research in other languages has increased, most of the work focuses on single languages. This paper explores the integration of features originating from multiple languages into a machine learning approach to subjectiv- ity analysis, and aims to show that this enriched feature set provides for more ef- fective modeling for the source as well as the target languages. We show not only that we are able to achieve over 75% macro accuracy in all of the six lan- guages we experiment with, but also that by using features drawn from multiple languages we can construct high-precision meta-classifiers with a precision of over 83%.