Combining Objective and Subjective Perspectives for Political News Understanding

TL;DR

This paper presents a novel text analysis framework that combines objective and subjective perspectives to analyze political news, offering fine-grained, explainable insights across different granularities and contexts.

Contribution

It introduces an integrated, bottom-up approach that enhances subjective analysis and explainability in political news content across multiple languages and regions.

Findings

Effective analysis of political news outlets and orientations

Insights into topics, entities, and demographics

Framework adaptable to various languages and countries

Abstract

Researchers and practitioners interested in computational politics rely on automatic content analysis tools to make sense of the large amount of political texts available on the Web. Such tools should provide objective and subjective aspects at different granularity levels to make the analyses useful in practice. Existing methods produce interesting insights for objective aspects, but are limited for subjective ones, are often limited to national contexts, and have limited explainability. We introduce a text analysis framework which integrates both perspectives and provides a fine-grained processing of subjective aspects. Information retrieval techniques and knowledge bases complement powerful natural language processing components to allow a flexible aggregation of results at different granularity levels. Importantly, the proposed bottom-up approach facilitates the explainability of the obtained results. We illustrate its functioning with insights on news outlets, political orientations, topics, individual entities, and demographic segments. The approach is instantiated on a large corpus of French news, but is designed to work seamlessly for other languages and countries.