acl acl2011 acl2011-289 acl2011-289-reference knowledge-graph by maker-knowledge-mining
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Author: Muhammad Abdul-Mageed ; Mona Diab ; Mohammed Korayem
Abstract: 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 ofModern 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 ofpreprocessing 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.
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