Abstract

In this thesis we investigate the automatic analysis of agreement and disagreement in political documents. Our focus is on the comparison of statements about specific topics extracted from documents with no direct interaction (e.g. electoral speeches or political manifestos), in which politicians may express, sometime in an implicit way, their position. This is a challenging task, made difficult also due to the lack of annotated resources. Our contribution can be divided into two main areas. The first one is the creation of manually and automatically annotated corpora for the task (pairs of statements annotated for agreement or disagreement from different sources). The second one is a Natural Language Processing (NLP) pipeline for the automatic (supervised) classification of agreement and disagreement. This pipeline involves a novel approach to extract well-defined and accurate topics based on key-concept clusters, and two classifiers to identify the pairs of statements in agreement and disagreement (or holding no relation) according to a wide set of features, such as sentiment, entailment, and semantic representation of the topics. We think that our findings can effectively support political science researchers dealing with an increasing amount of digital data, providing insight into similarities and differences in ideologies.