Publication Details

Modeling Review Argumentation for Robust Sentiment Analysis

authored by
Henning Wachsmuth, Martin Trenkmann, Benno Stein, Gregor Engels

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.

External Organisation(s)
Paderborn University
Bauhaus-Universität Weimar
Conference contribution
No. of pages
Publication date
Publication status
Peer reviewed
ASJC Scopus subject areas
Language and Linguistics, Linguistics and Language
Electronic version(s) (Access: Open)