Verb Forms in Emotion Recognition Tasks: Analysis of Data Characteristics and Classification Results
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Abstract
Emotion recognition in text represents an important task in natural language processing. This study investigates the influence of verb morphological form on emotion classification performance by encoder models and identifies factors that may determine morphological sensitivity in language models. Three models (ruBERT-tiny2, ruBERT-base, ruRoBERTa-large), fine-tuned on a combined corpus of four Russian-language datasets (22,852 examples), were employed in the experiment. Each model classified 5,676 morphological forms of 473 verbs from the Russian Emotion Lexicon. Results revealed systematic morphological influence: differences in classification performance between forms, as measured by F1-score, reached 20–25% across all models. To account for the observed patterns, training data characteristics were analysed. Frequency of morphological forms in the corpus was shown to partially correlate with classification performance, though it does not appear to be the sole determining factor. Analysis of emotion distribution across morphological forms revealed that in the training corpus, imperative forms occur predominantly in contexts associated with Joy. However, Sadness-labelled verbs in imperative mood are systematically classified by encoders as Anger. This discrepancy between training data characteristics and classification outcomes suggests that imperatives combined with sadness semantics are interpreted by models as expressions of aggression, indicating an interaction between grammatical form and verb semantics in determining emotion class.
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References
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