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Opinion mining is the task of extracting from a set of documents opinions expressed by a source on a specified target. This article presents a comparative study on the methods and resources that can be employed for mining opinions from quotations (reported speech) in newspaper articles. We show the difficulty of this task, motivated by the presence of different possible targets and the large variety of affect phenomena that quotes contain. We evaluate our approaches using annotated quotations extracted from news provided by the EMM news gathering engine. We conclude that a generic opinion mining system requires both the use of large lexicons, as well as specialised training and testing data.