Modeling Review Argumentation for Robust Sentiment Analysis

verfasst von

Henning Wachsmuth, Martin Trenkmann, Benno Stein, Gregor Engels

Abstract

Most text classification approaches model text at the lexical and syntactic level only, lacking domain robustness and explainability. In tasks like sentiment analysis, such approaches can result in limited effectiveness if the texts to be classified consist of a series of arguments. In this paper, we claim that even a shallow model of the argumentation of a text allows for an effective and more robust classification, while providing intuitive explanations of the classification results. Here, we apply this idea to the supervised prediction of sentiment scores for reviews. We combine existing approaches from sentiment analysis with novel features that compare the overall argumentation structure of the given review text to a learned set of common sentiment flow patterns. Our evaluation in two domains demonstrates the benefit of modeling argumentation for text classification in terms of effectiveness and robustness.

Externe Organisation(en)

Universität Paderborn
Bauhaus-Universität Weimar

Typ

Aufsatz in Konferenzband

Seiten

553-564

Anzahl der Seiten

12

Publikationsdatum

08.2014

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Sprache und Linguistik, Linguistik und Sprache

Elektronische Version(en)

https://aclanthology.org/C14-1053 (Zugang: Offen)