Active Learning for Detection of Stance Components

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Abstract

Automatic detection of five language components, which are all relevant for expressing opinions and for stance taking, was studied: positive sentiment, negative sentiment, speculation, contrast and condition. A resource-aware approach was taken, which included manual annotation of 500 training samples and the use of limited lexical resources. Active learning was compared to random selection of training data, as well as to a lexicon-based method. Active learning was successful for the categories speculation, contrast and condition, but not for the two sentiment categories, for which results achieved when using active learning were similar to those achieved when applying a random selection of training data. This difference is likely due to a larger variation in how sentiment is expressed than in how speakers express the other three categories. This larger variation was also shown by the lower recall results achieved by the lexicon-based approach for sentiment than for the categories speculation, contrast and condition.

Details

Authors
Organisations
External organisations
  • Gagavai AB
  • Linnaeus University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Specific Languages
  • Engineering and Technology

Keywords

  • active learning, stance, sentiment, annotation, classifier
Original languageEnglish
Title of host publicationThe 26th International Conference on Computational Linguistics
Subtitle of host publicationProceedings of COLING 2016
PublisherAssociation for Computational Linguistics
Pages50-59
ISBN (Electronic)978-4-87974-723-5
Publication statusPublished - 2016
Publication categoryResearch
Peer-reviewedYes
EventCOLING 2016 - Osaka International Convention Center , Osaka, Japan
Duration: 2016 Dec 112016 Dec 16
http://coling2016.anlp.jp/

Conference

ConferenceCOLING 2016
CountryJapan
CityOsaka
Period2016/12/112016/12/16
Internet address

Related projects

Carita Paradis, Andreas Kerren, Magnus Sahlgren, Kostiantyn Kucher, Maria Skeppstedt & Vasiliki Simaki

Swedish Research Council

2013/01/012018/01/02

Project: Research

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