Approaches for Automatically Tagging Affect

Nathanael Chambers, Joel Tetreault, and James Allen

The tagging of discourse is important not only for natural language processing research, but for many applications in the social sciences as well. This paper describes an evaluation of a range of different tagging techniques to automatically determine the attitude of speakers in transcribed psychiatric dialogues. It presents results in a marriage counseling domain that classifies the attitude and emotional commitment of the participants to a particular topic of discussion. It also gives results from the Switchboard Corpus to facilitate comparison for future work. Finally, it describes a new Java tool that learns attitude classifications using our techniques and provides a flexible, easy to use platform for tagging of texts.

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