Not All Segments are Created Equal: Syntactically Motivated Sentiment Analysis in Lexical Space
Although there is by now a considerable amount of research on subjectivity and sentiment analysis on morphologically-rich 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.
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