tailieunhanh - Báo cáo khoa học: "A Pilot Study of Implicit Attitude using Latent Textual Semantics"

We describe an unsupervised approach to the problem of automatically detecting subgroups of people holding similar opinions in a discussion thread. An intuitive way of identifying this is to detect the attitudes of discussants 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 detection of people’s attitudes in a discussion: if two persons share the same opinion, they tend to use similar language content. . | Genre Independent Subgroup Detection in Online Discussion Threads A Pilot Study of Implicit Attitude using Latent Textual Semantics Pradeep Dasigi Weiwei Guo Mona Diab pd2359@ weiwei@ mdiab@ Center for Computational Learning Systems Columbia University Abstract We describe an unsupervised approach to the problem of automatically detecting subgroups of people holding similar opinions in a discussion thread. An intuitive way of identifying this is to detect the attitudes of discussants 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 detection 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 paper we investigate the impact of implicit and explicit attitude in two genres of social media discussion data more formal wikipedia discussions and a debate discussion forum that is much more informal. Experimental results strongly suggest that implicit attitude is an important complement for explicit attitudes expressed via sentiment and it can improve the sub-group detection performance independent of genre. 1 Introduction There has been a significant increase in discussion forum data in online media recently. Most of such discussion threads have a clear debate component in them with varying levels of formality. Automatically identifying the groups of discussants with similar attitudes or subgroup detection is an interesting problem which allows for a better understanding of the data in this genre in a manner that could directly benefit Opinion Mining research as well as Community Mining from Social Networks. A straight-forward approach to this problem is to apply Opinion Mining techniques and extract 65 each discussant s attitudes towards other discussants and entities being .