Today we are more and more provided with information expressing opinions about different topics. In the same way, the number of Web sites giving a global score, usually by counting the number of stars for instance, is also growing extensively and this kind of tools can be very useful for users interested by having a general idea. Nevertheless, sometimes the expressed score (e.g. the number of stars) does not really reflect what it is ex- pressed in the text of a review. Actually, extracting opinions from texts is a problem that have been extensively addressed in the last decade and very efficient approaches are now proposed to extract the polarity of a text. In this presentation we focus on a topic related with opinion but rather than considering the full text we are interested with the opinions expressed on specific criteria. First we show how criteria can be automatically learnt. Second we illustrate how opinions are extracted. By considering criteria we illustrate that it is possible to propose new recommender systems but also to evaluate how opinions expressed on the criteria evolve over time.