Paper: Experimenting with Distant Supervision for Emotion Classification

ACL ID E12-1049
Title Experimenting with Distant Supervision for Emotion Classification
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

We describe a set of experiments using au- tomatically labelled data to train supervised classifiers for multi-class emotion detection in Twitter messages with no manual inter- vention. By cross-validating between mod- els trained on different labellings for the same six basic emotion classes, and testing on manually labelled data, we conclude that the method is suitable for some emotions (happiness, sadness and anger) but less able to distinguish others; and that different la- belling conventions are more suitable for some emotions than others.