Recognizing Stances in Ideological On-Line Debates

TLDR

The study investigates using sentiment and arguing opinions to classify stances in ideological online debates. An automatically constructed arguing lexicon from a manually annotated corpus is used to build supervised classifiers that incorporate sentiment, arguing opinions, and their targets as features. The classifiers outperform both a distribution‑based baseline and a unigram‑based system.

Abstract

This work explores the utility of sentiment and arguing opinions for classifying stances in ideological debates. In order to capture arguing opinions in ideological stance taking, we construct an arguing lexicon automatically from a manually annotated corpus. We build supervised systems employing sentiment and arguing opinions and their targets as features. Our systems perform substantially better than a distribution-based baseline. Additionally, by employing both types of opinion features, we are able to perform better than a unigram-based system.