AbstractExisting approaches to classifying documents by sentiment include machine learning with features created from n-grams and part of speech. This paper explores a different approach and examines performance of one selected machine learning algorithm, Support Vector Machines, with features computed using existing lexical resources. Special attention has been paid to fine tuning of the algorithm regarding number of features. The immediate purpose of this experiment is to evaluate lexical and sentiment resources in document-level sentiment classification task. Results described in the paper are also useful to indicate how lexicon design, different language dimensions and semantic categories contribute to document-level sentiment recognition. In a less direct way (through the examination of evaluated resources), the experiment analyzes adequacy of lexemes, word senses and synsets as different possible layers for ascribing sentiment, or as candidates for sentiment carriers. The proposed approach of machine learning word category frequencies instead of n-grams and part of speech features can potentially exhibit improvements in domain independency, but this hypothesis has to be verified in future works.