Preprint · Research Square · May 5, 2026
An annotated Uzbek social media corpus for emotion and sentiment analysis
Botir Elov, Alayev Ruhillo, Xusainova Zilola Yuldashevna
Abstract
Abstract UzEDSA is an annotated corpus for emotion detection and sentiment analysis in Uzbek social media comments. It contains 304,364 unique comments after batch merging, invalid-record removal, and duplicate filtering. Each comment is labelled with a multi-attribute schema covering primary emotion, multi-emotion annotation, sentiment polarity, emotional intensity, sarcasm, and domain. The emotion inventory includes JOY, SAD, ANG, FEA, SUR, TRU, DIS, ANT, MIX, and NEU. The corpus preserves social media features such as spelling variation, Uzbek Latin-Cyrillic script mixing, Uzbek-Russian code-switching, emojis, slang, repeated characters, markup artifacts, short reactions, and long comments. It covers general, personal, news, technology, medical, education, transport, service, and marketing domains. This Data Descriptor documents corpus construction, preprocessing, annotation, deduplication, validation, repository records, and usage notes. UzEDSA supports reusable research on low-resource Uzbek affective NLP, including emotion classification, polarity classification, intensity prediction, sarcasm-aware modelling, domain adaptation, and corpus-based analysis.
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