Scrutable Feature Sets for Stance Classification

Angrosh Mandya, Advaith Siddharthan, Adam Wyner

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

2 Citations (Scopus)


This paper describes and evaluates a novel feature set for stance classification of argumentative texts; i.e. deciding whether a post by a user is for or against the issue being debated.We model the debate both as attitude bearing features, including a set of automatically acquired ‘topic terms’ associated with a Distributional Lexical Model (DLM) that captures the writer’s attitude towards the topic term, and as dependency features that represent the points being made in the debate. The stance of the text towards the issue being debated is then learnt in a supervised framework as a function of these features. The main advantage of our feature set is that it is scrutable: The reasons for a classification can be explained to a human user in natural language. We also report that our method outperforms previous approaches to stance classification as well as a range of baselines based on sentiment analysis and topic-sentiment analysis.
Original languageEnglish
Title of host publicationProceedings of the 3rd Workshop on Argument Mining
PublisherACL Anthology
Number of pages10
ISBN (Electronic)978-1-945626-17-3
Publication statusPublished - 2016
Event3rd Workshop on Argument Mining: ACL 2016 - Berlin, Germany
Duration: 12 Aug 201612 Aug 2016 (3rd Workshop on Argument Mining - ACL 2016)


Conference3rd Workshop on Argument Mining
Abbreviated titleArgMining 2016
Internet address

Bibliographical note

This work was supported by the Economic and Social Research Council [Grant number ES/M001628/1].


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