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The paper describes the RuSentNE-2023 evaluation devoted to targetedsentiment analysis in Russian news texts. The task is to predict sentimenttowards a named entity in a single sentence. The dataset for RuSentNE-2023evaluation is based on the Russian news corpus RuSentNE having richsentiment-related annotation. The corpus is annotated with named entities andsentiments towards these entities, along with related effects and emotionalstates. The evaluation was organized using the CodaLab competition framework.The main evaluation measure was macro-averaged measure of positive and negativeclasses. The best results achieved were of 66% Macro F-measure(Positive+Negative classes). We also tested ChatGPT on the test set from ourevaluation and found that the zero-shot answers provided by ChatGPT reached 60%of the F-measure, which corresponds to 4th place in the evaluation. ChatGPTalso provided detailed explanations of its conclusion. This can be consideredas quite high for zero-shot application.

RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts