Paper: Subgroup Detector: A System for Detecting Subgroups in Online Discussions

ACL ID P12-3023
Title Subgroup Detector: A System for Detecting Subgroups in Online Discussions
Venue Annual Meeting of the Association of Computational Linguistics
Session System Demonstration
Year 2012
Authors

We present Subgroup Detector, a system for analyzing threaded discussions and identifying the attitude of discussants towards one another and towards the discussion topic. The system uses attitude predictions to detect the split of discussants into subgroups of opposing views. The system uses an unsupervised approach based on rule-based opinion target detecting and unsupervised clustering techniques. The system is open source and is freely available for download. An online demo of the system is available at: http://clair.eecs.umich.edu/SubgroupDetector/