Authors - Banu Yergesh, Tilekbergen Mukhamet, Manas Yergesh, Aisha Zhumagulova Abstract - Emotion recognition for Kazakh is constrained by limited labeled data and by indirect affective expression through idioms and culturally grounded lexical cues. This paper quantifies the contribution of emotion-annotated phraseology and a semantic knowledge base to seven-way (single-label) emotion classification (Ekman’s six basic emotions plus the Kazakh-specific shamerelated class, uiat). The aim of this study is to assess the contribution of emotionally annotated idioms and semantically labeled lexical units to emotion recognition in Kazakh texts. We hypothesize that both types of resources improve classification performance, while their combined use yields the strongest effect, especially for emotions with culturally and pragmatically marked meanings. We curate KazEmoPhras with 3379 emotion-bearing idioms in Kazakh, and two semantically tagged lexical resources (1,200 media units; 1,800 classical literature units). Fine-tuning XLM-RoBERTa on the augmented data improves accuracy from 68.3% to 74.9% and weighted F1 from 0.26 to 0.37. We additionally provide an impact-focused ablation protocol to isolate the effects of idiom and semantic features and discuss practical requirements for morphologyaware preprocessing in Kazakh.
