Lost in Discussion? Tracking Opinion Groups in Complex Political Discussions by the Example of the FOMC Meeting Transcriptions

The Federal Open Market Committee (FOMC) is a committee within the central banking system of the US and decides on the target rate. Analyzing the positions of its members is a challenge even for experts with a deep knowledge of the financial domain. In our work, we aim at automatically determining opinion groups in transcriptions of the FOMC discussions. We face two main challenges: first, the positions of the members are more complex as in common opinion mining tasks because they have more dimensions than pro or contra. Second, they cannot be learned as there is no labeled data available. We address the challenge using graph clustering methods to group the members, including the similarity of their speeches as well as agreement and disagreement they show towards each other in discussions. We show that our approach produces stable opinion clusters throughout successive meetings and correlates with positions of speakers on a dove-hawk scale estimated by experts.

[1]  Justin Grimmer,et al.  Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts , 2013, Political Analysis.

[2]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[3]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Dragomir R. Radev,et al.  Subgroup Detection in Ideological Discussions , 2012, ACL.

[5]  Marilyn A. Walker,et al.  Cats Rule and Dogs Drool!: Classifying Stance in Online Debate , 2011, WASSA@ACL.

[6]  Thomas M. Havrilesky,et al.  The Policy Preferences of FOMC Members as Revealed by Dissenting Votes , 1991 .

[7]  Dragomir R. Radev,et al.  How to Analyze Political Attention with Minimal Assumptions and Costs , 2010 .

[8]  Sean Gerrish,et al.  Predicting Legislative Roll Calls from Text , 2011, ICML.

[9]  Sven-Oliver Proksch,et al.  A Scaling Model for Estimating Time-Series Party Positions from Texts , 2007 .

[10]  Ed C. M. Noyons,et al.  A unified approach to mapping and clustering of bibliometric networks , 2010, J. Informetrics.

[11]  Swapna Somasundaran,et al.  Recognizing Stances in Online Debates , 2009, ACL.

[12]  Marilyn A. Walker,et al.  Stance Classification using Dialogic Properties of Persuasion , 2012, NAACL.

[13]  Dustin Hillard,et al.  Computer-Assisted Topic Classification for Mixed-Methods Social Science Research , 2008 .

[14]  Amita Misra,et al.  Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue , 2013, SIGDIAL Conference.

[15]  M. Laver,et al.  Extracting Policy Positions from Political Texts Using Words as Data , 2003, American Political Science Review.

[16]  Anne-Caroline Hüser.  Bankers, Bureaucrats and Central Bank Politics. The Myth of Neutrality , 2014 .

[17]  Justin Grimmer,et al.  A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases , 2010, Political Analysis.

[18]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[19]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[20]  Matt Thomas,et al.  Get out the vote: Determining support or opposition from Congressional floor-debate transcripts , 2006, EMNLP.