Multiple emotions are often evoked in readers in response to text stimuli like news article. In this paper, we present a method for classifying news sentences into multiple emotion categories. The corpus consists of 1000 news sentences and the emotion tag considered was anger, disgust, fear, happiness, sadness and surprise. We performed different experiments to compare the machine classification with human classification of emotion. In both the cases, it has been observed that combining anger and disgust class results in better classification and removing surprise, which is a highly ambiguous class in human classification, improves the performance. Words present in the sentences and the polarity of the subject, object and verb were used as features. The classifier performs better with the word and polarity feature combination compared to feature set consisting only of words. The best performance has been achieved with the corpus where anger and disgust classes are combined and surprise class is removed. In this experiment, the average precision was computed to be 79.5% and the average class wise micro F1 is found to be 59.52%.
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