Recognizing the emotive meaning of text can add another dimension to the understanding of text. We study the task of automatically categorizing sentences in a text into Ekman's six basic emotion categories. We experiment with corpus-based features as well as features derived from two emotion lexicons. One lexicon is automatically built using the classification system of Roget’s Thesaurus, while the other consists of words extracted from WordNet-Affect. Experiments on the data obtained from blogs show that a combination of corpus-based unigram features with emotion-related features provides superior classification performance. We achieve F-measure values that outperform the rule-based baseline method for all emotion classes.