期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2011
卷号：2011
出版社：ACL Anthology
摘要：Although Subjectivity and Sentiment Analysis
(SSA) has been witnessing a flurry of novel research,
there are few attempts to build SSA
systems for Morphologically-Rich Languages
(MRL). In the current study, we report efforts
to partially fill this gap. We present a newly
developed manually annotated corpus of Modern
Standard Arabic (MSA) together with a
new polarity lexicon.The corpus is a collection
of newswire documents annotated on the
sentence level. We also describe an automatic
SSA tagging system that exploits the annotated
data. We investigate the impact of different
levels of preprocessing settings on the SSA
classification task. We show that by explicitly
accounting for the rich morphology the system
is able to achieve significantly higher levels of
performance.
