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Although there is by now a considerable amount of research on subjectivity and sentiment analysis on morphologicallyrich languages, it is still unclear how lexical information can best be modeled in these languages. To bridge this gap, we build effective models exploiting exclusively gold and machine-segmented lexical input and successfully employ syntactically motivated feature selection to improve classification. Our best models achieve significantly above the baselines, with 67.93% and 69.37% accuracies for subjectivity and sentiment classification respectively.

Not All Segments are Created Equal: Syntactically Motivated Sentiment Analysis in Lexical Space