Twitter, as one of the popular social network services, is now widely used to query public opinions. In this paper, tweets, along with the reviews collected from review websites are used to carry out sentimental analysis, so as to figure out the language-based and location-based effects on user evaluations for six global restaurants. The language expansion is carried out that 34 languages are taken into account. By using a range of new and standard features, a series of classifiers are trained and applied in the later steps of sentiment analysis. Our experimental results show that the location and language effects on user evaluations for restaurants actually exist.