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Emotions are a key semantic component of human communication. This study focuses on automatic emotion detection in descriptive sentences and how this can be used to tune facial expression parameters for 3-D character generation. A comparison of manual and automatic word feature selection approaches is performed to determine the influence of word features on classification accuracy using support vector machines (SVM). The automatic emotion feature selection algorithm presented here builds on the framework used by mutual information for feature selection. Results of the study indicate that the set of automatically selected features was as good as the set of manually selected features. The proposed automatic feature selection algorithm implemented in this study helped to detect new words from the training corpus which were relevant to the classification task but were not considered by the researchers. An example of potential outcomes from facial expression tuning is also presented. The analysis includes initial results for dealing with the class imbalance challenge present in the data.