Extracting Sentiment Attitudes From Analytical Texts
This work addresses sentiment analysis in international relations texts, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of extracting sentiment relations between entities in analytical texts by creating the RuSentRel corpus and treating it as a three-class machine learning task, achieving results through experiments with conventional methods like Naive Bayes, SVM, and Random Forest, but no concrete numbers were provided.
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).