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This paper presents methods for mixing feature sets in sentence-level sentiment analysis where a sentence is classified into one of three classes: positive, negative, and neutral. Motivated by the need to classify sentences in Korean whose sentiment-revealing expressions tend to have different effects according to their syntactic categories, we employed a language modeling (LM) approach with 162 different LMs based on syntactic categories that are effectively combined with a Logistic Regressiondoi:10.1109/nlpke.2009.5313746 dblp:conf/nlpke/JeongKKMO09 fatcat:fzajrft5k5gwbi44pdubjmxnqu