Added RuSentNE-2023 evaluation #638
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+ RuSentNE-2023 evaluation
updated RuSentNE-2023
+ RuSentNE-2023
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https://arxiv.org/pdf/2305.17679.pdf
RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts.
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 Fmeasure (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