Paper: Genre Independent Subgroup Detection in Online Discussion Threads: A Study of Implicit Attitude using Textual Latent Semantics

ACL ID P12-2013
Title Genre Independent Subgroup Detection in Online Discussion Threads: A Study of Implicit Attitude using Textual Latent Semantics
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2012
Authors

We describe an unsupervised approach to the problem of automatically detecting sub- groups of people holding similar opinions in a discussion thread. An intuitive way of iden- tifying this is to detect the attitudes of discus- sants towards each other or named entities or topics mentioned in the discussion. Sentiment tags play an important role in this detection, but we also note another dimension to the de- tection of people?s attitudes in a discussion: if two persons share the same opinion, they tend to use similar language content. We consider the latter to be an implicit attitude. In this pa- per, we investigate the impact of implicit and explicit attitude in two genres of social media discussion data, more formal wikipedia dis- cussions and a debate discussion forum that is much more ...