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F1-scores for Single Domain evaluation. (Train, Dev, Test) sets for settings are the same as in Table 2, rows 1-3.
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In this article we present extended results obtained on the multidomain dataset of Polish text reviews collected within the Sentimenti project. We present preliminary results of classification models trained and tested on 7,000 texts annotated by over 20,000 individuals using valence, arousal, and eight basic emotions from Plutchik’s model. Additio...
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In the era of artificial intelligence, data is gold but costly to annotate. The paper demonstrates a groundbreaking solution to this dilemma using ChatGPT for text augmentation in sentiment analysis. We leverage ChatGPT's generative capabilities to create synthetic training data that significantly improves the performance of smaller models, making them competitive with, or even outperforming, their larger counterparts. This innovation enables models to be both efficient and effective, thereby reducing computational cost, inference time, and memory usage without compromising on quality. Our work marks a key advancement in the cost-effective development and deployment of robust sentiment analysis models.
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