Identifying Emotions, Intentions, and Attitudes in Text Using a Game with a Purpose

Subtle social information is available in text such as a speaker’s emotional state, intentions, and attitude, but current information extraction systems are unable to extract this information at the level that humans can. We describe a methodology for creating databases of messages annotated with social information based on interactive games between humans trying to generate and interpret messages for a number of different social information types. We then present some classification results achieved by using a small-scale database created with this methodology.