This paper studies sentiment analysis from the user-generated content on the Web. In particular, it focuses on mining opinions from comparative sentences, i.e., to determine which entities in a comparison are preferred by its author. A typical comparative sentence compares two or more entities. For example, the sentence, "the picture quality of Camera X is better than that of Camera Y", compares two entities "Camera X" and "Camera Y" with regard to their picture quality. Clearly, "Camera X" is

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... he preferred entity. Existing research has studied the problem of extracting some key elements in a comparative sentence. However, there is still no study of mining opinions from comparative sentences, i.e., identifying preferred entities of the author. This paper studies this problem, and proposes a technique to solve the problem. Our experiments using comparative sentences from product reviews and forum posts show that the approach is effective.