We develop MuSES, a multilingual sentiment identification system, which implements three different sentiment identification algorithms. Our first algorithm augments previous compositional semantic rules by adding social media-specific rules. In the second algorithm, we define a scoring function to measure the degree of a sentiment, instead of simply classifying a sentiment into binary polarities. All such scores are calculated based on a large volume of customer reviews. Due to the special characteristics of social media texts, we propose a third algorithm, which takes emoticons, negation word position, and domain-specific words into account. In addition, we propose a label-free process to transfer multilingual sentiment knowledge between different languages. We conduct our experiments on user comments from Facebook, tweets from Twitter, and multilingual product reviews from Amazon. We publish MuSES datasets online.