Paper: Modeling Review Argumentation for Robust Sentiment Analysis

ACL ID C14-1053
Title Modeling Review Argumentation for Robust Sentiment Analysis
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014

Most text classification approaches model text at the lexical and syntactic level only, lacking do- main 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 evalu- ation in...