RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts
This work addresses entity-oriented sentiment analysis for Russian language processing, providing a new benchmark but is incremental as it builds on existing datasets and methods.
The paper tackled the problem of targeted sentiment analysis on Russian news texts by evaluating models on the RuSentNE-2023 dataset, with the best result achieving 66% Macro F-measure and ChatGPT scoring 60% in zero-shot testing.
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.