Political Stance Detection using Knowledge Graphs and Sentiment Analysis

Abstract

Sentiment analysis techniques estimate the opinion of the au- thor of a text towards an entity from that text. Current sen- timent analysis techniques are based on language features or deep learning methods. However, they do not make use of the extensive background knowledge that human readers can have. This makes it difficult for these models to detect irony, pop culture references and other subtleties for which connec- tions between entities need to be known. The usage of knowl- edge graphs allows these models to use the enormous exist- ing knowledge bases. We propose a political stance detection pipeline that makes use of knowledge graphs and sentiment analysis. The proposed pipeline uses a combination of exist- ing deep learning methods and classic rule-based methods to train an opinion-aware knowledge graph, with which it clas- sifies sentences as either liberal, conservative or neutral. The pipeline acts as both a classifier and a framework that can in- tegrate existing stance detection models. In an experimental evaluation on the IBC and SemEval datasets, the proposed pipeline achieves an average F-score of 0.63, outperforming traditional machine learning models.