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In this work, we propose a method for automatic analysis of attitude (affect, judgment, and appreciation) in sentiment words. The first stage of the proposed method is an automatic separation of unambiguous affective and judgmental adjectives from those that express appreciation or different attitudes depending on context. In our experiments with machine learning algorithms we employed three feature sets based on Pointwise Mutual Information, word-pattern co-occurrence, and minimal path length. The next stage of the proposed method is to estimate the potentials of miscellaneous adjectives to convey affect, judgment, and appreciation. Based on the sentences automatically collected for each adjective, the algorithm analyses the context of phrases that contain sentiment words by considering morphological tags, high-level concepts, and named entities, and then makes decisions about contextual attitude labels. Finally, the appraisal potentials of a word are calculated based on the number of sentences related to each type of attitude. Our two-stage method was evaluated on two data sets, and promising results were obtained. The performance of our method was also compared with the method from previous work.