Sentiment attitudes and their extraction from analytical texts

Abstract

In this paper we study the task of extracting sentiment attitudes from analytical texts. We experiment with the RuSentRel corpus containing annotated Russian analytical texts in the sphere of international relations. Each document in the corpus is annotated with sentiments from the author to mentioned named entities, and attitudes between mentioned entities. We consider the problem of extracting sentiment relations between entities for the whole documents as a three-class machine learning task.