Detecting Perspectives in Political Debates

We explore how to detect people’s perspectives that occupy a certain proposition. We propose a Bayesian modelling approach where topics (or propositions) and their associated perspectives (or viewpoints) are modeled as latent variables. Words associated with topics or perspectives follow different generative routes. Based on the extracted perspectives, we can extract the top associated sentences from text to generate a succinct summary which allows a quick glimpse of the main viewpoints in a document. The model is evaluated on debates from the House of Commons of the UK Parliament, revealing perspectives from the debates without the use of labelled data and obtaining better results than previous related solutions under a variety of evaluations.

[1]  Amine Trabelsi,et al.  Finding Arguing Expressions of Divergent Viewpoints in Online Debates , 2014 .

[2]  Paolo Torroni,et al.  Argumentation Mining , 2016, ACM Trans. Internet Techn..

[3]  Serena Villata,et al.  Combining Textual Entailment and Argumentation Theory for Supporting Online Debates Interactions , 2012, ACL.

[4]  Diane J. Litman,et al.  Extracting Argument and Domain Words for Identifying Argument Components in Texts , 2015, ArgMining@HLT-NAACL.

[5]  Gerhard Weikum,et al.  Opinions network for politically controversial topics , 2012, PLEAD '12.

[6]  Jan Snajder,et al.  Back up your Stance: Recognizing Arguments in Online Discussions , 2014, ArgMining@ACL.

[7]  Manfred Stede,et al.  Joint prediction in MST-style discourse parsing for argumentation mining , 2015, EMNLP.

[8]  Noah A. Smith,et al.  Modeling User Arguments, Interactions, and Attributes for Stance Prediction in Online Debate Forums , 2015, SDM.

[9]  Luo Si,et al.  Mining contrastive opinions on political texts using cross-perspective topic model , 2012, WSDM '12.

[10]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[11]  Michael Röder,et al.  Exploring the Space of Topic Coherence Measures , 2015, WSDM.

[12]  Saif Mohammad,et al.  Stance and Sentiment in Tweets , 2016, ACM Trans. Internet Techn..

[13]  Saif Mohammad,et al.  SemEval-2016 Task 6: Detecting Stance in Tweets , 2016, *SEMEVAL.

[14]  Iryna Gurevych,et al.  Identifying Argumentative Discourse Structures in Persuasive Essays , 2014, EMNLP.

[15]  Claire Cardie,et al.  Annotating Expressions of Opinions and Emotions in Language , 2005, Lang. Resour. Evaluation.

[16]  Yulan He,et al.  Joint sentiment/topic model for sentiment analysis , 2009, CIKM.

[17]  Sanmay Das,et al.  Automated inference of point of view from user interactions in collective intelligence venues , 2014, ICML.

[18]  Jacob Cohen,et al.  Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. , 1968 .

[19]  G. Casella,et al.  Explaining the Gibbs Sampler , 1992 .

[20]  Guido Zarrella,et al.  MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection , 2016, *SEMEVAL.

[21]  T. Minka.  A comparison of numerical optimizers for logistic regression , 2004 .

[22]  Vangelis Karkaletsis,et al.  Argument Extraction from News , 2015, ArgMining@HLT-NAACL.

[23]  Stefan M. Rüger,et al.  Weakly Supervised Joint Sentiment-Topic Detection from Text , 2012, IEEE Transactions on Knowledge and Data Engineering.

[24]  Paolo Torroni,et al.  Argument Mining from Speech: Detecting Claims in Political Debates , 2016, AAAI.