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Questionnaire-based surveys and on-line product reviews resemble each other in that they both have user comments and satisfaction ratings. Since a comment might be a general opinion about a product or only one or a set of its attributes, in which case the text might not reflect the rating, surveys and reviews share the problem of pairing free-text comments with these ratings. To train accurate models for automatic evaluation of products from free-text, it is important to distinguish these two kinds of opinions. In this paper we present experiments on detecting general opinions that target a product as a whole, thus, reflect the user sentiments better. The task is different from subjectivity detection, since the goal is to detect generality of an opinion regardless of the rest of the documents being opinionated or not. The task complements feature-based opinion analysis and opinion polarity classification, since it can be applied as a preceding step to both tasks. We show that when used as a classification feature user ratings are not useful in the general opinion detection task. However, they are effective in predicting the polarity of a comment once it is identified as a general opinion.