Social context is an important part of human communication, hence it is also important for improved human computer interaction. One aspect of social context is the level of formality. Here, motivated by the difference observed between the emotional annotation of formal and informal dialogues in the HuComTech corpus, we introduce a content-free classification scheme based on feature sets designed for emotion recognition. With this method we attain an error rate of 8.8% in the classification of formal and informal dialogues, which means a relative error rate reduction of more than 40% compared to earlier results. By combining our proposed method with earlier models, we were able to further reduce the error rate to below 7%.
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