# Extracting Sentiment Attitudes From Analytical Texts

arXiv: https://arxiv.org/abs/1808.08932 · HTML: https://mlsanity.com/paper/1808.08932/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2018.

## Built on by (most cited first, 2 of 2)

- [RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts](https://mlsanity.com/paper/2305.17679/) (2023)
- [Current Landscape of the Russian Sentiment Corpora](https://mlsanity.com/paper/2106.14434/) (2021)

## Abstract (arXiv)

In this paper we present the RuSentRel corpus including analytical texts in the sphere of international relations. For each document we annotated sentiments from the author to mentioned named entities, and sentiments of relations between mentioned entities. In the current experiments, we considered the problem of extracting sentiment relations between entities for the whole documents as a three-class machine learning task. We experimented with conventional machine-learning methods (Naive Bayes, SVM, Random Forest).