In this paper we examine different linguistic features for sentimental polarity classification, and perform a comparative study on this task between blog and re- view data. We found that results on blog are much worse than reviews and investigated two methods to improve the performance on blogs. First we ex- plored information retrieval based topic analysis to extract relevant sentences to the given topics for po- larity classification. Second, we adopted an adaptive method where we train classifiers from review data and incorporate their hypothesis as features. Both methods yielded performance gain for polarity clas- sification on blog data.

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