Abstract: A key problem of sentiment analysis is to determine the polarity of a review is positive (thumbs up) or negative (thumbs down). Unlike topic-based text classification, where a high accuracy can be achieved, the sentiment classification is a hard and complicated task. One of the main challenges for document-level sentiment classification is that not every part of the document is equally informative for inferring the polarity of the whole document. Thus, making a distinction between key sentences and trivial sentences will be helpful to improve the sentiment classification performance. We divide a document into key sentences and detailed sentences. Key sentence is usually brief but discriminative while detailed sentences are diverse and ambiguous. For key sentence extraction, our approach takes three attributes into account: sentiment attribute, position attribute and special words attribute. To make use of the discrepancy and complementarity of key sentences and detailed sentences, we incorporate key sentences and detailed sentences in supervised and semi-supervised learning. In supervised sentiment classification, a classifier combination approach is adopted because the original document is divided into two different and complementary parts; in semi-supervised sentiment classification, a co-training algorithm is proposed to incorporate unlabeled data for sentiment classification. Experimental results across eight domains show that our approach performs better than the baseline method and the key sentence extraction is effective.