# Topic-Specific Sentiment Analysis Can Help Identify Political Ideology

arXiv: https://arxiv.org/abs/1810.12897 · HTML: https://mlsanity.com/paper/1810.12897/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2018.

## Built on by (most cited first, 10 of 10)

- [Analyzing Online Political Advertisements](https://mlsanity.com/paper/2105.04047/) (2021)
- [Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection](https://mlsanity.com/paper/2405.18974/) (2024)
- [Learning Unbiased News Article Representations: A Knowledge-Infused Approach](https://mlsanity.com/paper/2309.05981/) (2023)
- [Structuring the Space of Perspectives](https://mlsanity.com/paper/2608.12113/) (2026)
- [Text and author-level political inference using heterogeneous knowledge representations](https://mlsanity.com/paper/2206.12293/) (2022)
- [Language Models Learn Metadata: Political Stance Detection Case Study](https://mlsanity.com/paper/2409.13756/) (2024)
- [Social media polarization during conflict: Insights from an ideological stance dataset on Israel-Palestine Reddit comments](https://mlsanity.com/paper/2502.00414/) (2025)
- [Large Language Models for Sentiment Analysis to Detect Social Challenges: A Use Case with South African Languages](https://mlsanity.com/paper/2511.17301/) (2025)
- [Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles](https://mlsanity.com/paper/2606.06715/) (2026)
- [Ideological Stance Detection in a Low-Resource Language: Polarization in Bangladeshi Public vs Private University Discourse on Social Media](https://mlsanity.com/paper/2610.04401/) (2026)

## Abstract (arXiv)

Ideological leanings of an individual can often be gauged by the sentiment one expresses about different issues. We propose a simple framework that represents a political ideology as a distribution of sentiment polarities towards a set of topics. This representation can then be used to detect ideological leanings of documents (speeches, news articles, etc.) based on the sentiments expressed towards different topics. Experiments performed using a widely used dataset show the promise of our proposed approach that achieves comparable performance to other methods despite being much simpler and more interpretable.