In this paper we examine different linguistic features for sentimental polarity classification, and perform a comparative study on this task between blog and review data. We found that results on blog are much worse than reviews and investigated two methods to improve the performance on blogs. First we explored information retrieval based topic analysis to extract relevant sentences to the given topics for polarity 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 classification on blog data.
REF uses different linguistic features for sentiment classification.
Improving Blog Polarity Classification via Topic Analysis and Adaptive Methods
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Computer Science