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.