Sentiment analysis is a task to extract and organize authors' evaluations and opinions on focal subjects by analyzing massive amounts of text. This paper proposes a model of tree transfer from a syntactic tree to a set of semantic representations of sentiments. The method is based on deep syntactic and semantic information so that the outputs have suitable features for sentiment analysis applications: (1) to accurately detect the sentiment and its polarity and (2) to aggregate utterances which convey same or similar opinions. The proposed model can be designed analogously to a transfer-based method for machine translation, thus we can reuse several syntactic and semantic operations, such as combination of syntactic subtrees, case analysis of verb phrases and word sense disambiguation, and also several types of syntactic patterns. The experiments on Japanese sentiment extraction show that we acquired the sentiment expression in high-precision, the representation forms were informative than the naive ways of surface extraction and we can develop such a desirable sentiment extraction engine in a systematic way.