The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Detecting speculations, contrasts and conditionals in consumer reviews

På svenska

Author

Editor

  • Balahur Alexandra
  • van der Goot Erik
  • Vossen Piek
  • Montoyo Andrés

Summary, in English

A support vector classifier was compared to a lexicon-based approach for the task of detecting the stance categories speculation, contrast and conditional in English consumer reviews. Around 3,000 training instances were required to achieve a stable performance of an F-score of 90 for speculation. This outperformed the lexicon-based approach, for which an Fscore of just above 80 was achieved. The machine learning results for the other two categories showed a lower average (an approximate F-score of 60 for contrast and 70 for conditional), as well as a larger variance, and were only slightly better than lexicon matching. Therefore, while machine learning was successful for detecting speculation, a well-curated lexicon might be a more suitable approach for detecting contrast and conditional.

Department/s

Publishing year

2015

Language

English

Pages

162-168

Publication/Series

6th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, WASSA 2015 : Workshop proceedings

Document type

Conference paper

Publisher

Association for Computational Linguistics

Topic

  • Languages and Literature
  • Computer Science

Keywords

  • consumer reviews
  • support vector classifier
  • stance

Conference name

6th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA '15)

Conference date

2015-09-17

Status

Published

Project

  • StaViCTA - Advances in the description and explanation of stance in discourse using visual and computational text analytics

Research group

  • Language, Cognition and Discourse@Lund (LCD@L)

ISBN/ISSN/Other

  • ISBN: 978-1-941643-32-7