RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian
  News Texts
Anton Golubev Nicolay Rusnachenko Natalia Loukachevitch
The paper describes the RuSentNE-2023 evaluation devoted to targeted
sentiment analysis in Russian news texts. The task is to predict sentiment
towards a named entity in a single sentence. The dataset for RuSentNE-2023
evaluation is based on the Russian news corpus RuSentNE having rich
sentiment-related annotation. The corpus is annotated with named entities and
sentiments towards these entities, along with related effects and emotional
states. The evaluation was organized using the CodaLab competition framework.
The main evaluation measure was macro-averaged measure of positive and negative
classes. The best results achieved were of 66% Macro F-measure
(Positive+Negative classes). We also tested ChatGPT on the test set from our
evaluation and found that the zero-shot answers provided by ChatGPT reached 60%
of the F-measure, which corresponds to 4th place in the evaluation. ChatGPT
also provided detailed explanations of its conclusion. This can be considered
as quite high for zero-shot application.