Identifying Opinion-Topics and Polarity of Parliamentary Debate Motions

Gavin Abercrombie, Riza Theresa Batista-Navarro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Analysis of the topics mentioned and opinions expressed in parliamentary debate motions–or proposals–is difficult for human readers, but necessary for understanding and automatic processing of the content of the subsequent speeches. We present a dataset of debate motions with pre-existing 'policy' labels, and investigate the utility of these labels for simultaneous topic and opinion polarity analysis. For topic detection, we apply one-versus-the-rest supervised topic classification, finding that good performance is achieved in predicting the policy topics, and that textual features derived from the debate titles associated with the motions are particularly indicative of motion topic. We then examine whether the output could also be used to determine the positions taken by proposers towards the different policies by investigating how well humans agree in interpreting the opinion polarities of the motions. Finding very high levels of agreement, we conclude that the policies used can be reliable labels for use in these tasks, and that successful topic detection can therefore provide opinion analysis of the motions 'for free'.
Original languageEnglish
Title of host publicationProceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Place of PublicationUnited States
PublisherAssociation for Computational Linguistics
Pages280-285
DOIs
Publication statusPublished - 31 Oct 2018

Keywords

  • natural language processing
  • sentiment analysis
  • topic detection
  • Hansard
  • Hansard transcripts
  • NLP
  • civic technology
  • opinion mining

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