Approaches for Automatically Tagging Affect: Steps Toward an Effective and Efficient Tool

  • Nathanael Chambers
  • Joel Tetreault
  • James Allen
Part of the The Information Retrieval Series book series (INRE, volume 20)


The tagging of discourse is important not only for natural language processing research, but for many applications in the social sciences as well. This chapter 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.


affect automatic tagging cats stochastic affect affect tool psychological models 


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Copyright information

© Springer 2006

Authors and Affiliations

  • Nathanael Chambers
    • 1
  • Joel Tetreault
    • 2
  • James Allen
    • 2
  1. 1.The Institute for Human and Machine CognitionUSA
  2. 2.Department of Computer ScienceUniversity of RochesterUSA

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