# Combining Objective and Subjective Perspectives for Political News Understanding

arXiv: https://arxiv.org/abs/2408.11174 · HTML: https://mlsanity.com/paper/2408.11174/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 70 references that are in the map.
- First posted on arXiv 20 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [We Can Detect Your Bias: Predicting the Political Ideology of News Articles](https://mlsanity.com/paper/2010.05338/) (2020)
- [Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Experts](https://mlsanity.com/paper/2209.14557/) (2022)
- [Attentional Encoder Network for Targeted Sentiment Classification](https://mlsanity.com/paper/1902.09314/) (2019)
- [Sentiment Analysis in the News](https://mlsanity.com/paper/1309.6202/) (2013)
- [Using Word Embeddings to Examine Gender Bias in Dutch Newspapers, 1950-1990](https://mlsanity.com/paper/1907.08922/) (2019)
- [IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News Articles](https://mlsanity.com/paper/2007.12603/) (2020)
- [Framing the News:From Human Perception to Large Language Model Inferences](https://mlsanity.com/paper/2304.14456/) (2023)
- [Uncovering the structure of the French media ecosystem](https://mlsanity.com/paper/2107.12073/) (2021)
- [Learning Unbiased News Article Representations: A Knowledge-Infused Approach](https://mlsanity.com/paper/2309.05981/) (2023)

## Built on by (most cited first, 1 of 1)

- [Analyzing Political Bias in LLMs via Target-Oriented Sentiment Classification](https://mlsanity.com/paper/2505.19776/) (2025)

## Abstract (arXiv)

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.