UzMedSentiment: An Uzbek Medical-Domain Dataset for Aspect-Based Sentiment Analysis

UzMedSentiment is an Uzbek-language medical-domain dataset for aspect-based sentiment analysis and auxiliary linguistic annotation tasks. It contains 4,527 annotated text–aspect pairs representing 3,177 distinct text strings. The dataset covers ten aspects: medicines, symptoms, treatment and procedures, diagnostics, healthcare services, clinician attitude, infrastructure, price, waiting time, and diet. Each record includes a three-class sentiment label—negative (NEG), neutral (NEU), or positive (POS)—and a five-point polarity score ranging from −2 to +2. The sentiment distribution comprises 1,970 negative, 1,610 neutral, and 947 positive records. Additional annotations indicate adverse drug reaction (ADR) signals, negation, speculation, sarcasm, and textual cues. Source-category and language metadata are also included. The data are provided in UzMedSentiment.tsv, a tab-separated UTF-8 file with 13 columns: id, source, lang, text, aspect, sentiment, polarity_score, adr_flag, severity, negation, speculation, sarcasm, and cue_span. Severity is sparsely annotated, with only eight non-empty values; empty annotation fields should be treated as missing information. A single text can occur in multiple rows with different target aspects and sentiment labels. Consequently, each row represents a text–aspect pair rather than an independent document. No predefined training, validation, or test partitions are supplied. For evaluation, records sharing the same text should remain in the same partition to reduce data leakage. The dataset supports research on Uzbek sentiment classification, aspect-based sentiment analysis, polarity prediction, ADR signal detection, and linguistic cue detection. It is intended for computational research; its annotations do not establish clinical diagnoses or drug causality.

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