Sentiment Frames for Attitude Extraction in Russian
This work addresses the need for structured attitude extraction in Russian language processing, but it is incremental as it adapts existing frame-based methods to a new language.
The authors tackled the problem of extracting multiple types of attitude information from Russian texts by creating RuSentiFrames, a lexicon linking predicates to sentiment frames, and applied it to extract attitudes from a large news collection, demonstrating its utility in a real-world task.
Texts can convey several types of inter-related information concerning opinions and attitudes. Such information includes the author's attitude towards mentioned entities, attitudes of the entities towards each other, positive and negative effects on the entities in the described situations. In this paper, we described the lexicon RuSentiFrames for Russian, where predicate words and expressions are collected and linked to so-called sentiment frames conveying several types of presupposed information on attitudes and effects. We applied the created frames in the task of extracting attitudes from a large news collection.